diff --git a/1_instruction_tuning/notebooks/chat_templates_example.ipynb b/1_instruction_tuning/notebooks/chat_templates_example.ipynb index 93772206..8088448b 100644 --- a/1_instruction_tuning/notebooks/chat_templates_example.ipynb +++ b/1_instruction_tuning/notebooks/chat_templates_example.ipynb @@ -1,222 +1,5 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "vZAvFVIAtFlq" - }, - "source": [ - "# Exploring Chat Templates with SmolLM2\n", - "\n", - "This notebook demonstrates how to use chat templates with the `SmolLM2` model. Chat templates help structure interactions between users and AI models, ensuring consistent and contextually appropriate responses." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "K-lZu8JvtwUN", - "outputId": "c3871418-15bc-4265-ae8d-6d6036036d0e" - }, - "outputs": [], - "source": [ - "# Install the requirements in Google Colab\n", - "# !pip install transformers datasets trl huggingface_hub\n", - "\n", - "# Authenticate to Hugging Face\n", - "from huggingface_hub import login\n", - "\n", - "login()\n", - "\n", - "# for convenience you can create an environment variable containing your hub token as HF_TOKEN" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "tnHzBR7vtFlr" - }, - "outputs": [], - "source": [ - "# Import necessary libraries\n", - "from transformers import AutoModelForCausalLM, AutoTokenizer\n", - "from trl import setup_chat_format\n", - "import torch" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XTVOqbuetFlr" - }, - "source": [ - "## SmolLM2 Chat Template\n", - "\n", - "Let's explore how to use a chat template with the `SmolLM2` model. We'll define a simple conversation and apply the chat template." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 397, - "referenced_widgets": [ - "b922b90106414644bc0e933f28dea1bf", - "e0a40f83ae2e4ab29376a1d48b53aa6e", - "547eeb64ffd34e509c0b8b8ba6d657e2", - "45675fb5f5c94f8cae575582f7ae41a7", - "016d5e929f1240cea067372b2191d107", - "a026a32dd6d646bea82c1ebb06147d89", - "0479fd3fc1ba476ab46f8c0a98f89468", - "cbc312cb858b48a5a0f8dbcf60b7e684", - "f70401b6dba74380b19bd1ef887b3bf7", - "7eb91920e4384194a008902d6c4a09c7", - "b379da78cb34463aa5a72eedc3d176cd", - "ae2690497e024095adb3879643cffd33", - "f600aa1fe4094133888ec9a2504a60eb", - "efe9a9fcebfe441b80075fbfe9c32674", - "50dbf8861ca94b0ba1f4a7e2f0d8aead", - "547151540399460fb9a946bbe67afbd9", - "5291041c86db4933816088c047d659d8", - "48724ba7ba4e4f00923445245640739f", - "04ae3f7b640c42f3a8eb1977cd1a585d", - "db3bd55d779947028f36a8b24a2621b6", - "d17c62b889754b5d88cfced5b18ff7a7", - "990f706db474450ba0997d1dbcd53cb7", - "3b881514716c47308061fe85b810a6a4", - "26ed0f1bae204d74a313d101d9355e90", - "4ff5af1784904bc9b85515105885e2d8", - "b3c42d7e25d6494993029531adc3866d", - "6227b40396ea4024b3c8710c5e65601f", - "7612cc9b8908471b90c9118151d6e447", - "b687aca79e6e470b96254c5e309d6d63", - "3fa18e3b50104af796bd0887f556224a", - "4bfa3103048a47989a09a0d90ac6b9bf", - "85de66e1ee3140cf85eadebe5fea1e9f", - "b31de9bcf83e4070be09c7d663361232", - "d64d50101891491f96ff80162dc6d26c", - "d65ec0f0dc0b44e0869c6159e6e82ad6", - "76febcd912404a58add3a39f80a8218d", - "f4ea276bdc0d4da2a04b46e3f1ed95b5", - "0942430d36de4677b4c2fa771d7bcd2a", - "10a0f37020d44156a11e9750778892e0", - "58fb913274b54a60a832513c09608a2f", - "0bab42beb845475684e9e71dd1591e1d", - "89ecd1b28ab64c90afe3b9736fd48306", - "be4e145938054f13a510fe4d04a7a60d", - "648c3c820b39493daf0cce5f57a55467", - "01e0f8a799ad479eb95eef3e5a09bd70", - "8fe2df9a14a0436c9124a856ac7419e4", - "d108e029e743419989e30f64f0c82b90", - "bfd11f21f197459b8f27ef364bc9b264", - "76a0341ebe9f4c3face32460d7023be9", - "da1a999fb5af4eae9f6a9d1086cbb4cf", - "77f6c27c3c854138b4aa9789637141a1", - "6ceb292f2b8544f2a9a005d16d3e8978", - "41a27cf0a91246599d4d1b7dae7c7863", - "745fb1db425e44e5b3a23b36ae7675d1", - "bde95b39561145548fc81fb4cc94a1bf", - "3cc519fd92fe4b328943ec839115b63e", - "e15fc503bb73476980cedb5f06b51ced", - "d8c5dc8df3be4e65b2bbba020d29150f", - "c0177c4ad18740d88acfc603ce4735f8", - "eb570fd159124e2cbd2df9335b3f9cd6", - "5de5dab3d92f4f41838a8f302d27f0c3", - "471b481a3e5b4d439ab31fdc49fc99c7", - "7a0c705334694da6b750104b28db6dba", - "0c336ea5c653434da49e2f0e949f83d0", - "ec15d99b3a604405a2b4707931d4bf44", - "e7f5d507d9564941bb7db742b4bf01c7", - "aa2d32cb76ba47ebaa5ea391efbf58a7", - "7b20c7c8f6be40c6815b8531ecb9c936", - "e90b58981bd34d0e8f975fc1a9658c4c", - "5b7b09d983844f7893bdda411f9a0076", - "70f0eaed6ef14c2db8aecb592edeb1ad", - "d32017fa83aa44f6b2e3443a602654be", - "ff8debfb713f4b88be6b9b3bf33bfca2", - "ed577dea3ac54884a637ad775b42bc68", - "d43410dfcc8c4bebb8672f10ed2aeb66", - "0206fb9662a349c1aa8a6d87ce01c388", - "881b6196dfa0446e8c55a2420e484b6b", - "d54fb2da9f1f4a89ae962b8816314f43", - "77d3d81687e6417ab988b04984fc68f4", - "fbce0a69847e4099a55d1e39d4118c91", - "1513792bad534a0c9c381a131395c519", - "69f38fecf8ad403898634cfdfadf8925", - "17023310de9b4c3ebd8cc03758d59ef9", - "f3e23f781bce4429954d76bfea97aff4", - "530fc4c2bf1244628af7dea3e4b35cdf", - "96c2aae9198441569362135ad4bcbc98", - "76d306c21214412ab44e542d82e547aa", - "b9e41ef9e9c54fa7b71bc333604af74e" - ] - }, - "id": "Nrxh0oX6tFls", - "outputId": "953e1527-8168-4346-9338-6e188ca31a1a" - }, - "outputs": [], - "source": [ - "# Dynamically set the device\n", - "device = (\n", - " \"cuda\"\n", - " if torch.cuda.is_available()\n", - " else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n", - ")\n", - "\n", - "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n", - "model = AutoModelForCausalLM.from_pretrained(\n", - " pretrained_model_name_or_path=model_name\n", - ").to(device)\n", - "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n", - "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "zkJwILrbtFls" - }, - "outputs": [], - "source": [ - "# Define messages for SmolLM2\n", - "messages = [\n", - " {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n", - " {\n", - " \"role\": \"assistant\",\n", - " \"content\": \"I'm doing well, thank you! How can I assist you today?\",\n", - " },\n", - "]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Ve4dgtjstFls" - }, - "source": [ - "# Apply chat template without tokenization\n", - "\n", - "The tokenizer represents the conversation as a string with special tokens to describe the role of the user and the assistant.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "pbAg-5x-tFls", - "outputId": "5f9482db-1fcf-4c13-ccaa-ef3f6eff7f76" - }, - "outputs": [ + "cells": [ { "name": "stdout", "output_type": "stream", @@ -355,145 +138,7 @@ "text/plain": [ "" ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from IPython.core.display import display, HTML\n", - "\n", - "display(\n", - " HTML(\n", - " \"\"\"\n", - "\"\"\"\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - 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"outputs": [], - "source": [ - "from datasets import load_dataset\n", - "\n", - "ds = load_dataset(\"HuggingFaceTB/smoltalk\", \"everyday-conversations\")\n", - "\n", - "\n", - "def process_dataset(sample):\n", - " # TODO: ๐Ÿข Convert the sample into a chat format\n", - " # use the tokenizer's method to apply the chat template\n", - " return sample\n", - "\n", - "\n", - "ds = ds.map(process_dataset)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 381 }, - "id": "81fQeazltFlu", - "outputId": "36cf7148-9881-4f13-d0ce-76c82c4ab219" - }, - "outputs": [ { "data": { "text/html": [ @@ -511,5216 +156,30 @@ "metadata": {}, "output_type": "display_data" } - ], - "source": [ - "display(\n", - " HTML(\n", - " \"\"\"\n", - "\"\"\"\n", - " )\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "id": "bWUSv7NMtFlu" - }, - "outputs": [], - "source": [ - "ds = load_dataset(\"openai/gsm8k\", \"main\")\n", - "\n", - "\n", - "def process_dataset(sample):\n", - " # TODO: ๐Ÿ• Convert the sample into a chat format\n", - "\n", - " # 1. create a message format with the role and content\n", - "\n", - " # 2. apply the chat template to the samples using the tokenizer's method\n", - "\n", - " return sample\n", - "\n", - "\n", - "ds = ds.map(process_dataset)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "qlXCuRKotFlu" - }, - "source": [ - "## Conclusion\n", - "\n", - "This notebook demonstrated how to apply chat templates to different models, `SmolLM2`. By structuring interactions with chat templates, we can ensure that AI models provide consistent and contextually relevant responses.\n", - "\n", - "In the exercise you tried out converting a dataset into chatml format. Luckily, TRL will do this for you, but it's useful to understand what's going on under the hood." - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "py310", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.15" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "0055b6b628934affaf88bc58a1572bb6": { - "model_module": "@jupyter-widgets/controls", - "model_module_version": "1.5.0", - "model_name": "FloatProgressModel", - "state": { - "_dom_classes": [], - "_model_module": "@jupyter-widgets/controls", - "_model_module_version": "1.5.0", - "_model_name": "FloatProgressModel", - "_view_count": null, - "_view_module": "@jupyter-widgets/controls", - "_view_module_version": "1.5.0", - 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"cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Supervised Fine-Tuning with SFTTrainer\n", - "\n", - "This notebook demonstrates how to fine-tune the `HuggingFaceTB/SmolLM2-135M` model using the `SFTTrainer` from the `trl` library. The notebook cells run and will finetune the model. You can select your difficulty by trying out different datasets.\n", - "\n", - "
\n", - "

Exercise: Fine-Tuning SmolLM2 with SFTTrainer

\n", - "

Take a dataset from the Hugging Face hub and finetune a model on it.

\n", - "

Difficulty Levels

\n", - "

๐Ÿข Use the `HuggingFaceTB/smoltalk` dataset

\n", - "

๐Ÿ• Try out the `bigcode/the-stack-smol` dataset and finetune a code generation model on a specific subset `data/python`.

\n", - "

๐Ÿฆ Select a dataset that relates to a real world use case your interested in

\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Install the requirements in Google Colab\n", - "# !pip install transformers datasets trl huggingface_hub\n", - "\n", - "# Authenticate to Hugging Face\n", - "\n", - "from huggingface_hub import login\n", - "login()\n", - "\n", - "# for convenience you can create an environment variable containing your hub token as HF_TOKEN" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Import necessary libraries\n", - "from transformers import AutoModelForCausalLM, AutoTokenizer\n", - "from datasets import load_dataset\n", - "from trl import SFTConfig, SFTTrainer, setup_chat_format\n", - "import torch\n", - "\n", - "device = (\n", - " \"cuda\"\n", - " if torch.cuda.is_available()\n", - " else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n", - ")\n", - "\n", - "# Load the model and tokenizer\n", - "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n", - "model = AutoModelForCausalLM.from_pretrained(\n", - " pretrained_model_name_or_path=model_name\n", - ").to(device)\n", - "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n", - "\n", - "# Set up the chat format\n", - "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n", - "\n", - "# Set our name for the finetune to be saved &/ uploaded to\n", - "finetune_name = \"SmolLM2-FT-MyDataset\"\n", - "finetune_tags = [\"smol-course\", \"module_1\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Generate with the base model\n", - "\n", - "Here we will try out the base model which does not have a chat template. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Let's test the base model before training\n", - "prompt = \"Write a haiku about programming\"\n", - "\n", - "# Format with template\n", - "messages = [{\"role\": \"user\", \"content\": prompt}]\n", - "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n", - "\n", - "# Generate response\n", - "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n", - "outputs = model.generate(**inputs, max_new_tokens=100)\n", - "print(\"Before training:\")\n", - "print(tokenizer.decode(outputs[0], skip_special_tokens=True))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dataset Preparation\n", - "\n", - "We will load a sample dataset and format it for training. The dataset should be structured with input-output pairs, where each input is a prompt and the output is the expected response from the model.\n", - "\n", - "**TRL will format input messages based on the model's chat templates.** They need to be represented as a list of dictionaries with the keys: `role` and `content`,." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Load a sample dataset\n", - "from datasets import load_dataset\n", - "\n", - "# TODO: define your dataset and config using the path and name parameters\n", - "ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: ๐Ÿฆ If your dataset is not in a format that TRL can convert to the chat template, you will need to process it. Refer to the [module](../chat_templates.md)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configuring the SFTTrainer\n", - "\n", - "The `SFTTrainer` is configured with various parameters that control the training process. These include the number of training steps, batch size, learning rate, and evaluation strategy. Adjust these parameters based on your specific requirements and computational resources." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Configure the SFTTrainer\n", - "sft_config = SFTConfig(\n", - " output_dir=\"./sft_output\",\n", - " max_steps=1000, # Adjust based on dataset size and desired training duration\n", - " per_device_train_batch_size=4, # Set according to your GPU memory capacity\n", - " learning_rate=5e-5, # Common starting point for fine-tuning\n", - " logging_steps=10, # Frequency of logging training metrics\n", - " save_steps=100, # Frequency of saving model checkpoints\n", - " evaluation_strategy=\"steps\", # Evaluate the model at regular intervals\n", - " eval_steps=50, # Frequency of evaluation\n", - " use_mps_device=(\n", - " True if device == \"mps\" else False\n", - " ), # Use MPS for mixed precision training\n", - " hub_model_id=finetune_name, # Set a unique name for your model\n", - ")\n", - "\n", - "# Initialize the SFTTrainer\n", - "trainer = SFTTrainer(\n", - " model=model,\n", - " args=sft_config,\n", - " train_dataset=ds[\"train\"],\n", - " tokenizer=tokenizer,\n", - " eval_dataset=ds[\"test\"],\n", - ")\n", - "\n", - "# TODO: ๐Ÿฆ ๐Ÿ• align the SFTTrainer params with your chosen dataset. For example, if you are using the `bigcode/the-stack-smol` dataset, you will need to choose the `content` column`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Training the Model\n", - "\n", - "With the trainer configured, we can now proceed to train the model. The training process will involve iterating over the dataset, computing the loss, and updating the model's parameters to minimize this loss." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Train the model\n", - "trainer.train()\n", - "\n", - "# Save the model\n", - "trainer.save_model(f\"./{finetune_name}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "trainer.push_to_hub(tags=finetune_tags)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "

Bonus Exercise: Generate with fine-tuned model

\n", - "

๐Ÿ• Use the fine-tuned to model generate a response, just like with the base example..

\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Test the fine-tuned model on the same prompt\n", - "\n", - "# Let's test the base model before training\n", - "prompt = \"Write a haiku about programming\"\n", - "\n", - "# Format with template\n", - "messages = [{\"role\": \"user\", \"content\": prompt}]\n", - "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n", - "\n", - "# Generate response\n", - "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n", - "\n", - "# TODO: use the fine-tuned to model generate a response, just like with the base example." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ๐Ÿ’ You're done!\n", - "\n", - "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolLM2-135M` model using the `SFTTrainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n", - "\n", - "- Try this notebook on a harder difficulty\n", - "- Review a colleagues PR\n", - "- Improve the course material via an Issue or PR." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "py310", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.15" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/1_instruction_tuning/student_examples/Knight7561/python-code-DPO-fine-tune/sft_finetuning_example.ipynb b/1_instruction_tuning/student_examples/Knight7561/python-code-DPO-fine-tune/sft_finetuning_example.ipynb new file mode 100644 index 00000000..eb011a75 --- /dev/null +++ b/1_instruction_tuning/student_examples/Knight7561/python-code-DPO-fine-tune/sft_finetuning_example.ipynb @@ -0,0 +1,4101 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oSRmEnXierey" + }, + "source": [ + "# Supervised Fine-Tuning with SFTTrainer\n", + "\n", + "This notebook demonstrates how to fine-tune the `HuggingFaceTB/SmolLM2-135M` model using the `SFTTrainer` from the `trl` library. The notebook cells run and will finetune the model. You can select your difficulty by trying out different datasets.\n", + "\n", + "
\n", + "

Exercise: Fine-Tuning SmolLM2 with SFTTrainer

\n", + "

Take a dataset from the Hugging Face hub and finetune a model on it.

\n", + "

Difficulty Levels

\n", + "

๐Ÿข Use the `HuggingFaceTB/smoltalk` dataset

\n", + "

๐Ÿ• Try out the `bigcode/the-stack-smol` dataset and finetune a code generation model on a specific subset `data/python`.

\n", + "

๐Ÿฆ Select a dataset that relates to a real world use case your interested in

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Ah6aT0nRerez", + "outputId": "2a321c3c-3b4d-4433-827a-76a942a9288e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.47.1)\n", + "Collecting datasets\n", + " Downloading datasets-3.2.0-py3-none-any.whl.metadata (20 kB)\n", + "Collecting trl\n", + " Downloading trl-0.13.0-py3-none-any.whl.metadata (11 kB)\n", + "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.27.0)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n", + "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n", + "Requirement already satisfied: packaging>=20.0 in 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+ "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0mSuccessfully installed datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 trl-0.13.0 xxhash-3.5.0\n" + ] + } + ], + "source": [ + "# Install the requirements in Google Colab\n", + "!pip install transformers datasets trl huggingface_hub\n", + "\n", + "# Authenticate to Hugging Face\n", + "\n", + "# from huggingface_hub import login\n", + "# login()\n", + "\n", + "# for convenience you can create an environment variable containing your hub token as HF_TOKEN" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "PjNk89likfZT" + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ['WANDB_DISABLED'] = 'true'" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "wRat69HRere1", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 177, + "referenced_widgets": [ + "95cb4ce119334d5fae2d7e97970fc558", + "f0aa86663b574f35a52524fe58c044b9", + "649e63502812410d80386e3b55e591e0", + "81d589c9dffd4564989fd58d5b6d908e", + "eb1081a46626403da8a004613f0d289c", + "d75c0625bc6547e1b0682a93e9d418f4", + "ea97d2d8042b462b88c4bf88a218c3d4", + "cb46e9eb103a4d0fac94f2f5f2f272f3", + "c2916096377c4734b8e9da8330f24ea5", + "eac4c83e6b1942c4838d902d8e830f99", + "60400f00cf6b4e79b0bdf5a70202ece6", + "b08cba225f6843b0b2737dd6a16c18fc", + "c9a9d0bbc96448439d9bb1b25577e9f7", + "fc8499b7d7ba46f0be71999f6fffc499", + "fbebaaf2305c48fea74c7b74354a94a1", + "f6f33fa9be754e13a89e2508e1434a7e", + "0c9181fad40347c499a11720c3a38946", + "922140aed78442f98c20e0172343f028", + "fe68159ee67e484db54feb303d62680d", + "0f1ffae0817143ef91f20ffc9f5b7525", + "1c75c97cb847401483b445e7e7a83a16", + "ef9bf29e2adb4d88a08f3f861eadfa7e", + "cc82ecc5818b47639b5e9b4e4f11b0eb", + "57e2785f81a348419b9690bc94cb72d5", + "d567f388b3ad4840b1e81487bd2a0d81", + "937e664d3afd4825832b9123fcaf94c0", + "bdda45ce8e364104b05e57489e238715", + "60f09c4dfb3e49bbac45171b45f47cba", + "6fb8f77dbe684a4997020532b7fd3dc6", + "127f783d6d774954b63909fc89a96f9a", + "1e5d68aeb3774541b867c8a7e6b09c7d", + "210ccaae63a7438cbdb4c652ae55b791", + "825b92eff6854b62bb8854fcd912fd45", + "0501ee5cae28459cabd4382e02aea6b0", + "fb33760d00144d8cbcbf7ebe87b6fd16", + "a9b37785a9dd45db80902feb5f91f218", + "d0a34dfaf6a14072b7e820b72ca7d079", + "8462dcd3be8f4ff583f08ccf0f581722", + "2bf10cf31afd4cd381ec42ee1f330a92", + "1be8c7e55a6745a09748d90e5d583ce0", + "05b1b99af5954d79861b8d11cf0a5b55", + "07f245a1ca3040cb9b57b4473e8ced94", + "b5ce143df42f4f07a96bea8e5ac49d0e", + "fabbf1437113457f9c9c3bdf29fcd96a", + "56fd4c64e9a545f4803bcbe2293d3330", + "4d7db18f494c486baa705db45c89ed07", + "eaa4f7fbc4464a57a6d8fb437ba1e6d6", + "fa5a7505dc2a49f0944c9d60372efd96", + "72ef91e1df26421793f23e487d83e013", + "60842a27de6642c48b3c842171b60d86", + "362dfe534bad48a88295527d800f54a9", + "2dba5f3f853d4bd7bc2b6604776914b8", + "74cb21334b0a4f90b14a42063a0e031c", + "2d8a3e3650cc4347960b2f5339c33965", + "c3d08ce15be74b6e807f40b02987e64e" + ] + }, + "outputId": "0bce11aa-1962-4d6a-dd56-316b395c00a7" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "README.md: 0%| | 0.00/1.51k [00:00user\\nCompose a line of Python code to insert an element into a given list.<|im_end|>\\n<|im_start|>assistant\\nlist1 = [1, 3, 7, 8]\\nelement = 5\\n\\nlist1.insert(2, element)\\nprint(\"New list:\", list1)<|im_end|>\\n'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 22 + } + ] + }, + { + "cell_type": "code", + "source": [ + "test_finetuned_model(ds['train'][4]['prompt'])" + ], + "metadata": { + "id": "C9xFu7xXJc-n", + "outputId": "b379131e-c021-4c70-bacd-06b626f03d4c", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 86 + } + }, + "execution_count": 24, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "'user\\nOptimize the following Python code for faster runtime performance\\n\\nimport sys\\nimport os\\nimport time\\nimport random\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nimport matplotlib.animation as animation\\nimport matplotlib.dates as mdates\\nimport matplotlib.colors as mcolors\\nimport matplotlib.animation as animation\\nimport matplotlib.animation_examples as ani\\nimport matplotlib.animation as animation\\nimport matplotlib.animation_examples as ani\\nimport matplotlib.animation as animation\\nimport matplotlib.animation'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 24 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DFjTlhBhere2" + }, + "source": [ + "## Configuring the SFTTrainer\n", + "\n", + "The `SFTTrainer` is configured with various parameters that control the training process. These include the number of training steps, batch size, learning rate, and evaluation strategy. Adjust these parameters based on your specific requirements and computational resources." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 118, + "referenced_widgets": [ + "1b7fd9198a0b4a73ad0a756afd8a6e8c", + "7e955081c2704d7ab89c75f1ae99c795", + "d022ab604d954a77adc8e696280ca999", + "79c7039d594f4d4eb4a43c0d907d4c9d", + "c5bf05e0f87844188d3f9dea8137dd83", + "681d9d1f1e0442d6850263854f3236f3", + "6beb715163e9454ca04af05e026fb3db", + "7dd2b6c6a0a64f8986237b6fbaf908bf", + "6cfd9e16f6174c50a9d35715c74ea69f", + "f5dd669dd70e4ce0b9df23d29f61b466", + "5b900a7856ed4e7f94b05bd6821ba244", + "fd13b67c52e340f9a45ce1937d5b4bb2", + "48d1da187c4149898ba8ea4c73169c36", + "38a38b2b6eaa4a3f9b6e21e30cf4627f", + "1c6d1194c6454fa9ac434853cd8c4660", + "8634a1e407ec40589d1400ddfe4f7d1d", + "7c64dff1f00546b391006885de7cc8a4", + "94fab25ddbfa40338962038fe580236b", + "0c566f99d5f04b97a9862e6c3009ba9e", + "7b199596879648248d09bbc8f87db280", + "c10a90412af242159726f1edbd8cdc8e", + "b5eb8de496b34db2bc1a662957ee8649" + ] + }, + "id": "eHnEHqcOere2", + "outputId": "fa15cda5-5449-4085-e3f9-0126d179be87" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Map: 0%| | 0/1800 [00:00" + ], + "text/html": [ + "\n", + "
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" + ] + }, + "metadata": {} + } + ], + "source": [ + "# Train the model\n", + "trainer.train()\n", + "\n", + "# Save the model\n", + "trainer.save_model(f\"./{finetune_name}\")" + ] + }, + { + "cell_type": "markdown", + "source": [ + "The above training was stopped because there was no apperciable improvement observed in the epochs." + ], + "metadata": { + "id": "vqmO-kei_qM0" + } + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "id": "ZMOH1QW8ere2", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 139 + }, + "outputId": "049b8cf1-a212-406e-c2bc-11baae79ae7c" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Using the `WANDB_DISABLED` environment variable is deprecated and will be removed in v5. Use the --report_to flag to control the integrations used for logging result (for instance --report_to none).\n", + "No files have been modified since last commit. Skipping to prevent empty commit.\n", + "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "CommitInfo(commit_url='https://huggingface.co/knight7561/SmolLM2-morse-code/commit/7096164ef0de64108e1e4f76a7a3ad01692fe065', commit_message='End of training', commit_description='', oid='7096164ef0de64108e1e4f76a7a3ad01692fe065', pr_url=None, repo_url=RepoUrl('https://huggingface.co/knight7561/SmolLM2-morse-code', endpoint='https://huggingface.co', repo_type='model', repo_id='knight7561/SmolLM2-morse-code'), pr_revision=None, pr_num=None)" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 46 + } + ], + "source": [ + "# Set our name for the finetune to be saved &/ uploaded to\n", + "trainer.push_to_hub(tags=finetune_tags)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wqA-Pg6here2" + }, + "source": [ + "

\n", + "

Bonus Exercise: Generate with fine-tuned model

\n", + "

๐Ÿ• Use the fine-tuned to model generate a response, just like with the base example..

\n", + "
" + ] + }, + { + "cell_type": "code", + "source": [ + "model=model.from_pretrained('/content/sft_output/checkpoint-1000').to(device)" + ], + "metadata": { + "id": "SfxVqQXz41Hv" + }, + "execution_count": 32, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def test_on_dataset(ds_row):\n", + " print(\"####################\")\n", + " print(\"Model response: \\n\",test_finetuned_model(ds_row['prompt']))\n", + " print(\"Actual result: \\n\",ds_row['chosen'])" + ], + "metadata": { + "id": "PlJKfqAdzfbT" + }, + "execution_count": 38, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "for i in [42,68]:\n", + " print(\"\\n\\n\")\n", + " test_on_dataset(ds['validation'][i])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8dZDyoGf4PEK", + "outputId": "4f98fe1a-3706-4099-ad9a-de12dbc6fec7" + }, + "execution_count": 51, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\n", + "####################\n", + "Model response: \n", + " user\n", + "Optimize a Python program that squares an array of integers so that it consumes the least possible memory.\n", + "assistant\n", + "def square_array(arr):\n", + " return arr ** 2\n", + "\n", + "result = square_array([1, 2, 3, 4, 5])\n", + "print(result) # Output: 120\n", + "\n", + "# Optimized version\n", + "def square_array(arr):\n", + " return arr ** 2\n", + "\n", + "result = square_array([1, 2, 3, 4, 5])\n", + "print(result) # Output\n", + "Actual result: \n", + " def square_array(a):\n", + " res = [x ** 2 for x in a]\n", + " return res\n", + "\n", + "\n", + "\n", + "####################\n", + "Model response: \n", + " user\n", + "Write a function for converting a series of hex values into a decimal integer in Python.\n", + "assistant\n", + "def hex_to_decimal(hex_str):\n", + " return int(hex_str, 16)\n", + "\n", + "hex_str = \"0123456789ABCDEF\"\n", + "decimal_int = hex_to_decimal(hex_str)\n", + "print(decimal_int) # Output: 15\n", + "\n", + "# Output: 15\n", + "\n", + "# Input: 0123456789ABC\n", + "Actual result: \n", + " def convert_hexvals_to_decimal(hexvals):\n", + " # Split hexvals on whitespace\n", + " hexvals_list = hexvals.split()\n", + " # Reverse hexvals list\n", + " hexvals_list.reverse()\n", + " decimal_val = 0\n", + " # Loop through hexvals list\n", + " for index, hexval in enumerate(hexvals_list):\n", + " # Multiply hexval by 16^index to calculate decimal value\n", + " decimal_val += int(hexval, 16)*(16**index)\n", + " # Return decimal value\n", + " return decimal_val\n", + " \n", + "decimal = convert_hexvals_to_decimal(\"7a 0d 72\")\n", + "print(decimal)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G9Rv6iBJere3" + }, + "source": [ + "## ๐Ÿ’ You're done!\n", + "\n", + "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolLM2-135M` model using the `SFTTrainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n", + "\n", + "- Try this notebook on a harder difficulty\n", + "- Review a colleagues PR\n", + "- Improve the course material via an Issue or PR." + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "95cb4ce119334d5fae2d7e97970fc558": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": 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No newline at end of file diff --git a/1_instruction_tuning/notebooks/.env.example b/1_instruction_tuning/student_examples/aniketmishr/.env.example similarity index 100% rename from 1_instruction_tuning/notebooks/.env.example rename to 1_instruction_tuning/student_examples/aniketmishr/.env.example diff --git a/1_instruction_tuning/student_examples/aniketmishr/chat_templates_example.ipynb b/1_instruction_tuning/student_examples/aniketmishr/chat_templates_example.ipynb new file mode 100644 index 00000000..e1fefe92 --- /dev/null +++ b/1_instruction_tuning/student_examples/aniketmishr/chat_templates_example.ipynb @@ -0,0 +1,420 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "vZAvFVIAtFlq" + }, + "source": [ + "# Exploring Chat Templates with SmolLM2\n", + "\n", + "This notebook demonstrates how to use chat templates with the `SmolLM2` model. Chat templates help structure interactions between users and AI models, ensuring consistent and contextually appropriate responses." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "K-lZu8JvtwUN" + }, + "outputs": [], + "source": [ + "# Install the requirements in Google Colab\n", + "!pip install transformers datasets trl huggingface_hub\n", + "\n", + "# Authenticate to Hugging Face\n", + "from huggingface_hub import login\n", + "\n", + "login()\n", + "\n", + "# for convenience you can create an environment variable containing your hub token as HF_TOKEN" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "tnHzBR7vtFlr" + }, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "from trl import setup_chat_format\n", + "import torch" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XTVOqbuetFlr" + }, + "source": [ + "## SmolLM2 Chat Template\n", + "\n", + "Let's explore how to use a chat template with the `SmolLM2` model. We'll define a simple conversation and apply the chat template." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "Nrxh0oX6tFls" + }, + "outputs": [], + "source": [ + "# Dynamically set the device\n", + "device = (\n", + " \"cuda\"\n", + " if torch.cuda.is_available()\n", + " else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n", + ")\n", + "\n", + "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " pretrained_model_name_or_path=model_name\n", + ").to(device)\n", + "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n", + "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "zkJwILrbtFls" + }, + "outputs": [], + "source": [ + "# Define messages for SmolLM2\n", + "messages = [\n", + " {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": \"I'm doing well, thank you! How can I assist you today?\",\n", + " },\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ve4dgtjstFls" + }, + "source": [ + "# Apply chat template without tokenization\n", + "\n", + "The tokenizer represents the conversation as a string with special tokens to describe the role of the user and the assistant.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pbAg-5x-tFls", + "outputId": "dd5adad8-6a98-48f8-eaa6-c5271d1225a5" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Conversation with template: <|im_start|>user\n", + "Hello, how are you?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm doing well, thank you! How can I assist you today?<|im_end|>\n", + "\n" + ] + } + ], + "source": [ + "input_text = tokenizer.apply_chat_template(messages, tokenize=False)\n", + "\n", + "print(\"Conversation with template:\", input_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sfvdglOqtFls" + }, + "source": [ + "# Decode the conversation\n", + "\n", + "Note that the conversation is represented as above but with a further assistant message.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mXUVdPeytFls", + "outputId": "16c52bee-aa58-4a4c-bd9e-fe0cf3def3e9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Conversation decoded: <|im_start|>user\n", + "Hello, how are you?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm doing well, thank you! How can I assist you today?<|im_end|>\n", + "<|im_start|>assistant\n", + "\n" + ] + } + ], + "source": [ + "input_text = tokenizer.apply_chat_template(\n", + " messages, tokenize=True, add_generation_prompt=True\n", + ")\n", + "\n", + "print(\"Conversation decoded:\", tokenizer.decode(token_ids=input_text))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UcZQpspEtFlt" + }, + "source": [ + "# Tokenize the conversation\n", + "\n", + "Of course, the tokenizer also tokenizes the conversation and special token as ids that relate to the model's vocabulary.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jc2PLxAMtFlt", + "outputId": "450fab64-ad81-4aae-b180-697edc3312ad" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Conversation tokenized: [1, 4093, 198, 19556, 28, 638, 359, 346, 47, 2, 198, 1, 520, 9531, 198, 57, 5248, 2567, 876, 28, 9984, 346, 17, 1073, 416, 339, 4237, 346, 1834, 47, 2, 198, 1, 520, 9531, 198]\n" + ] + } + ], + "source": [ + "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)\n", + "\n", + "print(\"Conversation tokenized:\", input_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m3eNp9a0tFlt" + }, + "source": [ + "
\n", + "

Exercise: Process a dataset for SFT

\n", + "

Take a dataset from the Hugging Face hub and process it for SFT.

\n", + "

Difficulty Levels

\n", + "

๐Ÿข Convert the `HuggingFaceTB/smoltalk` dataset into chatml format.

\n", + "

๐Ÿ• Convert the `openai/gsm8k` dataset into chatml format.

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qbkXV2_ItFlt", + "collapsed": true + }, + "outputs": [], + "source": [ + "from IPython.core.display import display, HTML\n", + "\n", + "display(\n", + " HTML(\n", + " \"\"\"\n", + "\"\"\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4p3atw4_tFlu", + "outputId": "c90a92f0-2bb7-4691-e1e8-a5d2a8d641af" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'full_topic': 'Work/Career development/Mentorship',\n", + " 'messages': [{'content': 'Hi', 'role': 'user'},\n", + " {'content': 'Hello! How can I help you today?', 'role': 'assistant'},\n", + " {'content': \"I'm looking for career advice. I want to find a new job, but I'm not sure what I want to do.\",\n", + " 'role': 'user'},\n", + " {'content': 'Career development can be challenging. What are your current skills and interests that might help narrow down some options?',\n", + " 'role': 'assistant'},\n", + " {'content': \"I have experience in marketing and enjoy working with people. I'm also interested in learning more about data analysis.\",\n", + " 'role': 'user'},\n", + " {'content': \"That's a great combination. You might consider roles like marketing analyst or business development, which combine people skills with data analysis. I can provide more information on those careers if you'd like.\",\n", + " 'role': 'assistant'},\n", + " {'content': 'That sounds helpful. Can you also suggest some resources for learning data analysis?',\n", + " 'role': 'user'},\n", + " {'content': 'Absolutely. Online courses like Coursera, edX, and LinkedIn Learning offer a wide range of data analysis courses. Additionally, you can explore professional networks like LinkedIn groups or attend industry events to learn from others in the field.',\n", + " 'role': 'assistant'}]}" + ] + }, + "metadata": {}, + "execution_count": 20 + } + ], + "source": [ + "from datasets import load_dataset\n", + "\n", + "ds = load_dataset(\"HuggingFaceTB/smoltalk\", \"everyday-conversations\")\n", + "\n", + "\n", + "def process_dataset(sample):\n", + " # TODO: ๐Ÿข Convert the sample into a chat format\n", + "\n", + " return sample\n", + "\n", + "\n", + "ds = ds.map(process_dataset)\n", + "ds[\"train\"][1]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "81fQeazltFlu" + }, + "outputs": [], + "source": [ + "display(\n", + " HTML(\n", + " \"\"\"\n", + "\"\"\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": true, + "id": "bWUSv7NMtFlu", + "outputId": "e3d5bba4-4356-44fd-bad8-57f56099d7fe", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "{'question': 'Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn?',\n", + " 'answer': 'Weng earns 12/60 = $<<12/60=0.2>>0.2 per minute.\\nWorking 50 minutes, she earned 0.2 x 50 = $<<0.2*50=10>>10.\\n#### 10',\n", + " 'messages': [{'content': 'Weng earns $12 an hour for babysitting. Yesterday, she just did 50 minutes of babysitting. How much did she earn?',\n", + " 'role': 'user'},\n", + " {'content': 'Weng earns 12/60 = $<<12/60=0.2>>0.2 per minute.\\nWorking 50 minutes, she earned 0.2 x 50 = $<<0.2*50=10>>10.\\n#### 10',\n", + " 'role': 'assistant'}]}" + ] + }, + "metadata": {}, + "execution_count": 22 + } + ], + "source": [ + "ds = load_dataset(\"openai/gsm8k\", \"main\")\n", + "\n", + "\n", + "def process_dataset(sample):\n", + " # TODO: ๐Ÿ• Convert the sample into a chat format\n", + "\n", + " messages=[\n", + "\n", + " {\"role\": \"user\", \"content\": sample['question']},\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": sample['answer'],\n", + " },\n", + " ]\n", + " return {\"messages\":messages}\n", + "\n", + "\n", + "ds = ds.map(process_dataset)\n", + "ds['train'][1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qlXCuRKotFlu" + }, + "source": [ + "## Conclusion\n", + "\n", + "This notebook demonstrated how to apply chat templates to different models, `SmolLM2`. By structuring interactions with chat templates, we can ensure that AI models provide consistent and contextually relevant responses.\n", + "\n", + "In the exercise you tried out converting a dataset into chatml format. Luckily, TRL will do this for you, but it's useful to understand what's going on under the hood." + ] + } + ], + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + }, + "accelerator": "GPU" + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/1_instruction_tuning/student_examples/aniketmishr/sft_finetuning_example.ipynb b/1_instruction_tuning/student_examples/aniketmishr/sft_finetuning_example.ipynb new file mode 100644 index 00000000..f1cc891e --- /dev/null +++ b/1_instruction_tuning/student_examples/aniketmishr/sft_finetuning_example.ipynb @@ -0,0 +1,2972 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "mZ4H4qp5hmab" + }, + "source": [ + "# Supervised Fine-Tuning with SFTTrainer\n", + "\n", + "This notebook demonstrates how to fine-tune the `HuggingFaceTB/SmolLM2-135M` model using the `SFTTrainer` from the `trl` library. The notebook cells run and will finetune the model. You can select your difficulty by trying out different datasets.\n", + "\n", + "
\n", + "

Exercise: Fine-Tuning SmolLM2 with SFTTrainer

\n", + "

Take a dataset from the Hugging Face hub and finetune a model on it.

\n", + "

Difficulty Levels

\n", + "

๐Ÿข Use the `HuggingFaceTB/smoltalk` dataset

\n", + "

๐Ÿ• Try out the `bigcode/the-stack-smol` dataset and finetune a code generation model on a specific subset `data/python`.

\n", + "

๐Ÿฆ Select a dataset that relates to a real world use case your interested in

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "AWKZHOJYhmad", + "outputId": "a8f4b8ed-b1fb-4476-d89b-581c56b907a7", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 37, + "referenced_widgets": [ + "faef070528b842cd883a1ea14123765c", + "1f410022be3d4da1b2ea2736694e1de8", + "70026ac243ab4f5699c919f54ef5a7c9", + "96de48c897984c8aaf9edf9756dff40d", + "71f83f8cba0d4037af177d1f5eb77a22", + "fc2637f8db43438a9a900fce9aef6287", + "8b1144f02b224201a9dbe6d86ec538d0", + "38965216eeff4af69dec0d96188fe16e", + "fc3d0da352d447ada1051d01dceee4e0", + "03b4a9bed7374a8596ab8fc476e935d1", + "47b1fa2ca2014b7caab85fc11527174c", + "ec0db731099a40dbb11cba3a23537cca", + "93881ef7b9f845d7aed932e9816a815c", + "3406be2375c2490eac45212713b81e62", + "a2f3eaeadb8b471e9b8bfb55cc0eb43b", + "05c7f93590b84927bf0cd547989adde0", + "d4401d719cf04bb3985b0912da385a6a", + "e36a2941f02c4465bd11c70e9b5a43b2", + "fa7616d14b2d44ad9f5027e6ab611068", + "0c3fc413248348068f3a603c0f721387" + ] + } + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "VBox(children=(HTML(value='
\n", + "

Bonus Exercise: Generate with fine-tuned model

\n", + "

๐Ÿ• Use the fine-tuned to model generate a response, just like with the base example..

\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "mo2kKgBWhmar", + "outputId": "ccc79674-c83d-48fc-aefd-5b16e77ae366", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "user\n", + "Why can't light escape from a black hole?\n", + "assistant\n", + "Because it can't. You can try to explain that. But the answer is that light is a particle, and that particles are always affected by forces.\n", + "\n", + "If an object is in orbit, it is being pulled towards the center of the galaxy. This means that it will always be at a higher energy state than when it was stationary, which means that the energy of that object will have to be transferred to another object. This is called radiation, and is a consequence of the conservation of energy. If you want to explain that, you'd better explain the radiation.\n", + "\n", + "But the radiation is not the only force that is at work. If the object is moving faster than the speed of light, then the radiation is no longer a force, but a velocity. If the object is moving faster than the speed of light, the energy of the object is transferred to another object. This is what is meant by the conservation of energy\n" + ] + } + ], + "source": [ + "# Test the fine-tuned model on the same prompt\n", + "\n", + "# Let's test the base model before training\n", + "prompt = \"Why can't light escape from a black hole?\"\n", + "\n", + "# Format with template\n", + "messages = [{\"role\": \"user\", \"content\": prompt}]\n", + "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n", + "\n", + "# Generate response\n", + "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n", + "\n", + "# TODO: use the fine-tuned to model generate a response, just like with the base example.\n", + "ft_model =AutoModelForCausalLM.from_pretrained(f\"./{finetune_name}\").to(device)\n", + "outputs = ft_model.generate(\n", + " **inputs,\n", + " max_new_tokens=200,\n", + " eos_token_id=tokenizer.eos_token_id,\n", + " pad_token_id=tokenizer.eos_token_id,\n", + " do_sample=True,\n", + " top_k=10,\n", + " temperature=0.8,\n", + ")\n", + "print(tokenizer.decode(outputs[0], skip_special_tokens = True))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wP2tALXFhmar" + }, + "source": [ + "## ๐Ÿ’ You're done!\n", + "\n", + "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolLM2-135M` model using the `SFTTrainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n", + "\n", + "- Try this notebook on a harder difficulty\n", + "- Review a colleagues PR\n", + "- Improve the course material via an Issue or PR." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + }, + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "accelerator": "GPU", + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "faef070528b842cd883a1ea14123765c": { + "model_module": "@jupyter-widgets/controls", + "model_name": "VBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "VBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "VBoxView", + "box_style": "", + "children": [], + "layout": "IPY_MODEL_8b1144f02b224201a9dbe6d86ec538d0" + } + }, + "1f410022be3d4da1b2ea2736694e1de8": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_38965216eeff4af69dec0d96188fe16e", + "placeholder": "โ€‹", + "style": "IPY_MODEL_fc3d0da352d447ada1051d01dceee4e0", + "value": "

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Chat Templates with SmolLM2\n", + "\n", + "This notebook demonstrates how to use chat templates with the `SmolLM2` model. Chat templates help structure interactions between users and AI models, ensuring consistent and contextually appropriate responses." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K-lZu8JvtwUN", + "outputId": "c3871418-15bc-4265-ae8d-6d6036036d0e" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "33b2f5d2c4144be8a76e98af92783fb9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='
user\n", + "Hello, how are you?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm doing well, thank you! How can I assist you today?<|im_end|>\n", + "\n" + ] + } + ], + "source": [ + "input_text = tokenizer.apply_chat_template(messages, tokenize=False)\n", + "\n", + "print(\"Conversation with template:\", input_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sfvdglOqtFls" + }, + "source": [ + "# Decode the conversation\n", + "\n", + "Note that the conversation is represented as above but with a further assistant message.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mXUVdPeytFls", + "outputId": "80870e53-7bc1-426e-ac33-ba6748e030fc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conversation decoded: <|im_start|>user\n", + "Hello, how are you?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm doing well, thank you! How can I assist you today?<|im_end|>\n", + "<|im_start|>assistant\n", + "\n" + ] + } + ], + "source": [ + "input_text = tokenizer.apply_chat_template(\n", + " messages, tokenize=True, add_generation_prompt=True\n", + ")\n", + "\n", + "print(\"Conversation decoded:\", tokenizer.decode(token_ids=input_text))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UcZQpspEtFlt" + }, + "source": [ + "# Tokenize the conversation\n", + "\n", + "Of course, the tokenizer also tokenizes the conversation and special token as ids that relate to the model's vocabulary.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jc2PLxAMtFlt", + "outputId": "d2098780-b3f4-41ec-a1f3-b6da2b593c62" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conversation tokenized: [1, 4093, 198, 19556, 28, 638, 359, 346, 47, 2, 198, 1, 520, 9531, 198, 57, 5248, 2567, 876, 28, 9984, 346, 17, 1073, 416, 339, 4237, 346, 1834, 47, 2, 198, 1, 520, 9531, 198]\n" + ] + } + ], + "source": [ + "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)\n", + "\n", + "print(\"Conversation tokenized:\", input_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m3eNp9a0tFlt" + }, + "source": [ + "
\n", + "

Exercise: Process a dataset for SFT

\n", + "

Take a dataset from the Hugging Face hub and process it for SFT.

\n", + "

Difficulty Levels

\n", + "

๐Ÿข Convert the `HuggingFaceTB/smoltalk` dataset into chatml format.

\n", + "

๐Ÿ• Convert the `openai/gsm8k` dataset into chatml format.

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 381 + }, + "id": "qbkXV2_ItFlt", + "outputId": "06deadc3-2c63-4660-d2bd-05096ef07c9f" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_27925/4068687702.py:1: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython.display\n", + " from IPython.core.display import display, HTML\n" + ] + }, + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.core.display import display, HTML\n", + "\n", + "display(\n", + " HTML(\n", + " \"\"\"\n", + "\"\"\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241, + "referenced_widgets": [ + "c2d74a42fb574b8892d0a288fd92f0a6", + "056b9ef5706843b19cd62fce75743afb", + "17b4d81e40564a53bb79be9fbef4918e", + "951f60cddcb84dfdbbdf2058369f0541", + "646484cf7a36444daebe1dfe4a0e4150", + "e2f0c39ce1c046e8acb150dfbfaf5aa8", + "7eb12d70d2b542a7b651c7680f590279", + "ea1f9cb22abf4e7d9f6e76fc86c03387", + "00c9f5ca71b84df4b26acee72c97fefb", + "505f96bc0c7843bcb1498ba1c1ba5f06", + "635cc2881a1e4b8788bb26c356740e04", + "a6ee323c13904525a99c6f092ba96e18", + "67fffe7d6f8c4963972b408529e05532", + "0055b6b628934affaf88bc58a1572bb6", + "aafbbb9fc5164fa3a88193bfd33d2f79", + "606e39d53ed64967a60337418c71c595", + "26b15fa18b1b4963a1ba76a76675e7ee", + "db09ab1f79db4f3a8de77f0348eca0f7", + "de04f344a8d4428e8ba1836a563d8aa1", + "03c09673186046d799d6f487d6623e6b", + "1cc682330b24431b8812c73041e987d0", + "dafe748a452148038779f6a62a22a4ec", + "addad1c100024c44a0959978153da9a8", + "9bea2a23db644ad19b708d10e35d54ee", + "d1174b127571420593971166fbb1966b", + "add90ed3746d4293a1b71198137a892c", + "8def25e6389f4e6192b517b6e80aa05e", + "c9747e7a810f413ba1ea108307e3ad1d", + "d0ea49d1d90f4d34bf2ae70efa96946e", + "59d0997b85614384bbfebeee928340b6", + "269920491c134501873e0110367bc984", + "384d26051c04460e8870a3ffe9406c48", + "8e8a0e89a50646c897e546c4077db79e", + "ff60308921f9432683acbcd6d29fb78f", + "3bc8f6339f4e4a3b961d810255c5573e", + "4780ad263ec04b1a97525d985e102049", + "488feef55878426bbf1c753c6d58735b", + "560ba45d70ca431dadeb327d234c330a", + "04d0a6f74af346f7bc696951949063c8", + "2a18ce941b0f4cef8307988ef898b47f", + "194e3fda3635466b998f96e3dc22746a", + "e2ab3cb38b5a41f68d18ed5f0e6ae22c", + "f0b271bcac6c43a9aaddac54259bb514", + "0dc93d50a283472f9ca64fd0a4c6ff15", + "dd1a50d4497144388a1809b78bb38f58", + "6b72a856e5bd4812a5e0dd0c3bfb8455", + "4e21a567d1f6461985727823b37166e1", + "ec1efb7598fd496bb170673ae1b8a1df", + "84f393468aa74baa903243d238b2d387", + "a54ce365be104d27aaa15cf8c63b5ebe", + "1791220377d141ac9b307246177d0712", + "fa330d4f0fb241aebd065f6ef4a6892c", + "cfa1cc6eed8a4f7791a7959308456b6b", + "b50c9c4433854cf7a6b2593e946b7faa", + "7557cd24ba9b4aa3955866d59db94519", + "cc608dfb880c49d4bc5acf2d691b8ec6", + "cb838c5bed994a9a8e6fcf5c98b76d17", + "76bbe8c2beba4c0594085d32a68d2ee7", + "c9836c952b07472880649b82e2347e8d", + "383db57f997140d482b82b123080837a", + "182abc7ec4d944d9bb2ec1281c98b4c8", + "6934c6d1cbac44dbb08f3fffe3056edb", + "05fa0f6eb78b4c56b219b0e57521bd2e", + "012aa94e3cf24e32833c6bbca23c52f7", + "76c1a1cdc9054bbe90d0d3b662cf0ed1", + "e453f1672772400a851735ba64f42c8b", + "d1358f6b16644cb3a2328ca639a4a77a", + "c19f60d4028045399c62004027eaafd9", + "8055588a1fa940239c801ef66f3ecf3b", + "7468a9bc8bda44e5b44574c64fdc6803", + "a13a8f8b702e44ed88c7d358a0a8b4b4", + "13367fbb763747fa8de94cde40ffae32", + "b1fcf477db664ccdade4096fb79de327", + "9d1c06ac6b774d82adca58773f389161", + "31910159cf30463b8246ec47ffd8ab5b", + "72220420f9d340eabec13a01caebc92c", + "55b14c03a41c495aacf8ac2d0f96ba0b" + ] + }, + "id": "4p3atw4_tFlu", + "outputId": "62ee9812-3819-4a9c-9e24-5687368ffcd8" + }, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "\n", + "ds = load_dataset(\"HuggingFaceTB/smoltalk\", \"everyday-conversations\")\n", + "\n", + "def process_dataset(sample):\n", + " # This seems to be in the right format already??\n", + " return sample\n", + "\n", + "\n", + "ds = ds.map(process_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 381 + }, + "id": "81fQeazltFlu", + "outputId": "36cf7148-9881-4f13-d0ce-76c82c4ab219" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\n", + " HTML(\n", + " \"\"\"\n", + "\"\"\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": true, + "id": "bWUSv7NMtFlu" + }, + "outputs": [], + "source": [ + "ds = load_dataset(\"openai/gsm8k\", \"main\")\n", + "\n", + "\n", + "def process_dataset(sample):\n", + " question, answer = sample[\"question\"], sample[\"answer\"]\n", + " messages = [{\"role\": \"user\", \"content\": question}, {\"role\": \"assistant\", \"content\": answer}]\n", + " \n", + " return {\"messages\": messages}\n", + "\n", + "\n", + "ds = ds.map(process_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qlXCuRKotFlu" + }, + "source": [ + "## Conclusion\n", + "\n", + "This notebook demonstrated how to apply chat templates to different models, `SmolLM2`. By structuring interactions with chat templates, we can ensure that AI models provide consistent and contextually relevant responses.\n", + "\n", + "In the exercise you tried out converting a dataset into chatml format. Luckily, TRL will do this for you, but it's useful to understand what's going on under the hood." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "0055b6b628934affaf88bc58a1572bb6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + 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The notebook cells run and will finetune the model. You can select your difficulty by trying out different datasets.\n", + "\n", + "
\n", + "

Exercise: Fine-Tuning SmolLM2 with SFTTrainer

\n", + "

Take a dataset from the Hugging Face hub and finetune a model on it.

\n", + "

Difficulty Levels

\n", + "

๐Ÿข Use the `HuggingFaceTB/smoltalk` dataset

\n", + "

๐Ÿ• Try out the `bigcode/the-stack-smol` dataset and finetune a code generation model on a specific subset `data/python`.

\n", + "

๐Ÿฆ Select a dataset that relates to a real world use case your interested in

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f6dcc6187e26407ea39327150491f231", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='
\n", + " \n", + " \n", + " [1000/1000 06:36, Epoch 1/2]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
StepTraining LossValidation Loss
2001.5379001.499897
4001.3404001.417641
6001.2788001.360425
8001.2598001.328012
10001.0487001.316831

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Train the model\n", + "trainer.train()\n", + "\n", + "# Save the model\n", + "trainer.save_model(f\"./{finetune_name}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "48ed33e58c5244ffa7c248cf59d7db93", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "model.safetensors: 0%| | 0.00/538M [00:00\n", + "

Bonus Exercise: Generate with fine-tuned model

\n", + "

๐Ÿ• Use the fine-tuned to model generate a response, just like with the base example..

\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "After training:\n", + "user\n", + "Write a haiku about programming\n", + "assistant\n", + "Coding, a secret code,\n", + "Intricate patterns unfold,\n", + "Life's secrets hidden.\n", + "Nature's code, revealed.\n", + "Code, a secret path,\n", + "Life's secrets, hidden.\n", + "Nature's code, revealed.\n", + "Life's secrets, hidden.\n", + "Coding, a secret path,\n", + "Life's secrets, hidden.\n", + "Nature's code, revealed.\n", + "Life's secrets, hidden.\n", + "Coding, a secret path,\n", + "Life's secrets\n" + ] + } + ], + "source": [ + "# Test the fine-tuned model on the same prompt\n", + "\n", + "# Let's test the base model before training\n", + "prompt = \"Write a haiku about programming\"\n", + "\n", + "# Format with template\n", + "messages = [{\"role\": \"user\", \"content\": prompt}]\n", + "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n", + "\n", + "# Generate response\n", + "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens=100)\n", + "print(\"After training:\")\n", + "print(tokenizer.decode(outputs[0], skip_special_tokens=True))" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "After training:\n", + "user\n", + "Write a haiku about an awesome online course for learning about transformers\n", + "assistant\n", + "Transformers, they're here,\n", + "In the vast ocean of code,\n", + "Learning, they're here.\n", + "Nature's power, they know.\n", + "Transformers, they know.\n", + "Learning, they're here.\n", + "Nature's power, they know.\n", + "Transformers, they know.\n", + "Learning, they're here.\n", + "Nature's power, they know.\n", + "Transformers, they know.\n", + "Learning, they're here.\n", + "Nature's power, they know.\n" + ] + } + ], + "source": [ + "prompt = \"Write a haiku about an awesome online course for learning about transformers\"\n", + "\n", + "# Format with template\n", + "messages = [{\"role\": \"user\", \"content\": prompt}]\n", + "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n", + "\n", + "# Generate response\n", + "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens=100)\n", + "print(\"After training:\")\n", + "print(tokenizer.decode(outputs[0], skip_special_tokens=True))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ๐Ÿ’ You're done!\n", + "\n", + "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolLM2-135M` model using the `SFTTrainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n", + "\n", + "- Try this notebook on a harder difficulty\n", + "- Review a colleagues PR\n", + "- Improve the course material via an Issue or PR." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/1_instruction_tuning/student_examples/jucanbe/jucanbe_chat_templates_example.ipynb b/1_instruction_tuning/student_examples/jucanbe/jucanbe_chat_templates_example.ipynb new file mode 100644 index 00000000..23afaae9 --- /dev/null +++ b/1_instruction_tuning/student_examples/jucanbe/jucanbe_chat_templates_example.ipynb @@ -0,0 +1,5860 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "vZAvFVIAtFlq" + }, + "source": [ + "# Exploring Chat Templates with SmolLM2\n", + "\n", + "This notebook demonstrates how to use chat templates with the `SmolLM2` model. Chat templates help structure interactions between users and AI models, ensuring consistent and contextually appropriate responses." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K-lZu8JvtwUN", + "outputId": "c3871418-15bc-4265-ae8d-6d6036036d0e", + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "37ee3ea3ac114e11902c40e7106c9fea", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='
user\n", + "Hello, how are you?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm doing well, thank you! How can I assist you today?<|im_end|>\n", + "\n" + ] + } + ], + "source": [ + "input_text = tokenizer.apply_chat_template(messages, tokenize=False)\n", + "\n", + "print(\"Conversation with template:\", input_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sfvdglOqtFls" + }, + "source": [ + "# Decode the conversation\n", + "\n", + "Note that the conversation is represented as above but with a further assistant message.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mXUVdPeytFls", + "outputId": "80870e53-7bc1-426e-ac33-ba6748e030fc", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conversation decoded: <|im_start|>user\n", + "Hello, how are you?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm doing well, thank you! How can I assist you today?<|im_end|>\n", + "<|im_start|>assistant\n", + "\n" + ] + } + ], + "source": [ + "input_text = tokenizer.apply_chat_template(\n", + " messages, tokenize=True, add_generation_prompt=True\n", + ")\n", + "\n", + "print(\"Conversation decoded:\", tokenizer.decode(token_ids=input_text))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UcZQpspEtFlt" + }, + "source": [ + "# Tokenize the conversation\n", + "\n", + "Of course, the tokenizer also tokenizes the conversation and special token as ids that relate to the model's vocabulary.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jc2PLxAMtFlt", + "outputId": "d2098780-b3f4-41ec-a1f3-b6da2b593c62", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conversation tokenized: [1, 4093, 198, 19556, 28, 638, 359, 346, 47, 2, 198, 1, 520, 9531, 198, 57, 5248, 2567, 876, 28, 9984, 346, 17, 1073, 416, 339, 4237, 346, 1834, 47, 2, 198, 1, 520, 9531, 198]\n" + ] + } + ], + "source": [ + "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)\n", + "\n", + "print(\"Conversation tokenized:\", input_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m3eNp9a0tFlt" + }, + "source": [ + "
\n", + "

Exercise: Process a dataset for SFT

\n", + "

Take a dataset from the Hugging Face hub and process it for SFT.

\n", + "

Difficulty Levels

\n", + "

๐Ÿข Convert the `HuggingFaceTB/smoltalk` dataset into chatml format.

\n", + "

๐Ÿ• Convert the `openai/gsm8k` dataset into chatml format.

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 381 + }, + "id": "qbkXV2_ItFlt", + "outputId": "06deadc3-2c63-4660-d2bd-05096ef07c9f", + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\juanc\\AppData\\Local\\Temp\\ipykernel_16872\\4068687702.py:1: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython.display\n", + " from IPython.core.display import display, HTML\n" + ] + }, + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.core.display import display, HTML\n", + "\n", + "display(\n", + " HTML(\n", + " \"\"\"\n", + "\"\"\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241, + "referenced_widgets": [ + "c2d74a42fb574b8892d0a288fd92f0a6", + "056b9ef5706843b19cd62fce75743afb", + "17b4d81e40564a53bb79be9fbef4918e", + "951f60cddcb84dfdbbdf2058369f0541", + "646484cf7a36444daebe1dfe4a0e4150", + "e2f0c39ce1c046e8acb150dfbfaf5aa8", + "7eb12d70d2b542a7b651c7680f590279", + "ea1f9cb22abf4e7d9f6e76fc86c03387", + "00c9f5ca71b84df4b26acee72c97fefb", + "505f96bc0c7843bcb1498ba1c1ba5f06", + "635cc2881a1e4b8788bb26c356740e04", + "a6ee323c13904525a99c6f092ba96e18", + "67fffe7d6f8c4963972b408529e05532", + "0055b6b628934affaf88bc58a1572bb6", + "aafbbb9fc5164fa3a88193bfd33d2f79", + "606e39d53ed64967a60337418c71c595", + "26b15fa18b1b4963a1ba76a76675e7ee", + "db09ab1f79db4f3a8de77f0348eca0f7", + "de04f344a8d4428e8ba1836a563d8aa1", + "03c09673186046d799d6f487d6623e6b", + "1cc682330b24431b8812c73041e987d0", + "dafe748a452148038779f6a62a22a4ec", + "addad1c100024c44a0959978153da9a8", + "9bea2a23db644ad19b708d10e35d54ee", + "d1174b127571420593971166fbb1966b", + "add90ed3746d4293a1b71198137a892c", + "8def25e6389f4e6192b517b6e80aa05e", + "c9747e7a810f413ba1ea108307e3ad1d", + "d0ea49d1d90f4d34bf2ae70efa96946e", + "59d0997b85614384bbfebeee928340b6", + "269920491c134501873e0110367bc984", + "384d26051c04460e8870a3ffe9406c48", + "8e8a0e89a50646c897e546c4077db79e", + "ff60308921f9432683acbcd6d29fb78f", + "3bc8f6339f4e4a3b961d810255c5573e", + "4780ad263ec04b1a97525d985e102049", + "488feef55878426bbf1c753c6d58735b", + "560ba45d70ca431dadeb327d234c330a", + "04d0a6f74af346f7bc696951949063c8", + "2a18ce941b0f4cef8307988ef898b47f", + "194e3fda3635466b998f96e3dc22746a", + "e2ab3cb38b5a41f68d18ed5f0e6ae22c", + "f0b271bcac6c43a9aaddac54259bb514", + "0dc93d50a283472f9ca64fd0a4c6ff15", + "dd1a50d4497144388a1809b78bb38f58", + "6b72a856e5bd4812a5e0dd0c3bfb8455", + "4e21a567d1f6461985727823b37166e1", + "ec1efb7598fd496bb170673ae1b8a1df", + "84f393468aa74baa903243d238b2d387", + "a54ce365be104d27aaa15cf8c63b5ebe", + "1791220377d141ac9b307246177d0712", + "fa330d4f0fb241aebd065f6ef4a6892c", + "cfa1cc6eed8a4f7791a7959308456b6b", + "b50c9c4433854cf7a6b2593e946b7faa", + "7557cd24ba9b4aa3955866d59db94519", + "cc608dfb880c49d4bc5acf2d691b8ec6", + "cb838c5bed994a9a8e6fcf5c98b76d17", + "76bbe8c2beba4c0594085d32a68d2ee7", + "c9836c952b07472880649b82e2347e8d", + "383db57f997140d482b82b123080837a", + "182abc7ec4d944d9bb2ec1281c98b4c8", + "6934c6d1cbac44dbb08f3fffe3056edb", + "05fa0f6eb78b4c56b219b0e57521bd2e", + "012aa94e3cf24e32833c6bbca23c52f7", + "76c1a1cdc9054bbe90d0d3b662cf0ed1", + "e453f1672772400a851735ba64f42c8b", + "d1358f6b16644cb3a2328ca639a4a77a", + "c19f60d4028045399c62004027eaafd9", + "8055588a1fa940239c801ef66f3ecf3b", + "7468a9bc8bda44e5b44574c64fdc6803", + "a13a8f8b702e44ed88c7d358a0a8b4b4", + "13367fbb763747fa8de94cde40ffae32", + "b1fcf477db664ccdade4096fb79de327", + "9d1c06ac6b774d82adca58773f389161", + "31910159cf30463b8246ec47ffd8ab5b", + "72220420f9d340eabec13a01caebc92c", + "55b14c03a41c495aacf8ac2d0f96ba0b" + ] + }, + "id": "4p3atw4_tFlu", + "outputId": "62ee9812-3819-4a9c-9e24-5687368ffcd8", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['full_topic', 'messages'],\n", + " num_rows: 2260\n", + " })\n", + " test: Dataset({\n", + " features: ['full_topic', 'messages'],\n", + " num_rows: 119\n", + " })\n", + "})\n", + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['full_topic', 'messages'],\n", + " num_rows: 2260\n", + " })\n", + " test: Dataset({\n", + " features: ['full_topic', 'messages'],\n", + " num_rows: 119\n", + " })\n", + "})\n", + "[{'content': 'Hi there', 'role': 'user'}, {'content': 'Hello! How can I help you today?', 'role': 'assistant'}, {'content': \"I'm looking for a beach resort for my next vacation. Can you recommend some popular ones?\", 'role': 'user'}, {'content': \"Some popular beach resorts include Maui in Hawaii, the Maldives, and the Bahamas. They're known for their beautiful beaches and crystal-clear waters.\", 'role': 'assistant'}, {'content': 'That sounds great. Are there any resorts in the Caribbean that are good for families?', 'role': 'user'}, {'content': 'Yes, the Turks and Caicos Islands and Barbados are excellent choices for family-friendly resorts in the Caribbean. They offer a range of activities and amenities suitable for all ages.', 'role': 'assistant'}, {'content': \"Okay, I'll look into those. Thanks for the recommendations!\", 'role': 'user'}, {'content': \"You're welcome. I hope you find the perfect resort for your vacation.\", 'role': 'assistant'}]\n", + "[{'content': 'Hey!', 'role': 'user'}, {'content': 'Hello! How can I help you today?', 'role': 'assistant'}, {'content': \"I'm planning a trip to Paris. What are some popular tourist attractions?\", 'role': 'user'}, {'content': 'The Eiffel Tower, the Louvre Museum, and Notre Dame Cathedral are must-visit places in Paris.', 'role': 'assistant'}, {'content': 'That sounds great. Are there any local markets I should check out?', 'role': 'user'}, {'content': 'Yes, the Champs-ร‰lysรฉes Christmas Market and the Marchรฉ aux Puces de Saint-Ouen (flea market) are very popular among tourists and locals alike.', 'role': 'assistant'}, {'content': 'Awesome, thank you for the recommendations!', 'role': 'user'}, {'content': \"You're welcome! Have a great time in Paris!\", 'role': 'assistant'}]\n", + "Train conversation decoded: <|im_start|>user\n", + "Hi there<|im_end|>\n", + "<|im_start|>assistant\n", + "Hello! How can I help you today?<|im_end|>\n", + "<|im_start|>user\n", + "I'm looking for a beach resort for my next vacation. Can you recommend some popular ones?<|im_end|>\n", + "<|im_start|>assistant\n", + "Some popular beach resorts include Maui in Hawaii, the Maldives, and the Bahamas. They're known for their beautiful beaches and crystal-clear waters.<|im_end|>\n", + "<|im_start|>user\n", + "That sounds great. Are there any resorts in the Caribbean that are good for families?<|im_end|>\n", + "<|im_start|>assistant\n", + "Yes, the Turks and Caicos Islands and Barbados are excellent choices for family-friendly resorts in the Caribbean. They offer a range of activities and amenities suitable for all ages.<|im_end|>\n", + "<|im_start|>user\n", + "Okay, I'll look into those. Thanks for the recommendations!<|im_end|>\n", + "<|im_start|>assistant\n", + "You're welcome. I hope you find the perfect resort for your vacation.<|im_end|>\n", + "<|im_start|>assistant\n", + "\n" + ] + } + ], + "source": [ + "from datasets import load_dataset\n", + "\n", + "ds = load_dataset(\"HuggingFaceTB/smoltalk\", \"everyday-conversations\")\n", + "\n", + "print(ds)\n", + "\n", + "def process_dataset(sample):\n", + " return sample\n", + "\n", + "ds = ds.map(process_dataset)\n", + "\n", + "print(ds)\n", + "train_sample = ds[\"train\"][0][\"messages\"]\n", + "print(train_sample)\n", + "test_sample = ds[\"test\"][0][\"messages\"]\n", + "print(test_sample)\n", + "\n", + "input_text = tokenizer.apply_chat_template(\n", + " train_sample, tokenize=True, add_generation_prompt=True\n", + ")\n", + "\n", + "print(\"Train conversation decoded:\", tokenizer.decode(token_ids=input_text))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 381 + }, + "id": "81fQeazltFlu", + "outputId": "36cf7148-9881-4f13-d0ce-76c82c4ab219", + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\n", + " HTML(\n", + " \"\"\"\n", + "\"\"\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "bWUSv7NMtFlu", + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['question', 'answer', 'messages'],\n", + " num_rows: 7473\n", + " })\n", + " test: Dataset({\n", + " features: ['question', 'answer', 'messages'],\n", + " num_rows: 1319\n", + " })\n", + "})\n", + "[{'content': 'Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?', 'role': 'user'}, {'content': 'Natalia sold 48/2 = <<48/2=24>>24 clips in May.\\nNatalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\\n#### 72', 'role': 'assistant'}]\n", + "[{'content': \"Janetโ€™s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?\", 'role': 'user'}, {'content': 'Janet sells 16 - 3 - 4 = <<16-3-4=9>>9 duck eggs a day.\\nShe makes 9 * 2 = $<<9*2=18>>18 every day at the farmerโ€™s market.\\n#### 18', 'role': 'assistant'}]\n", + "Train conversation decoded: <|im_start|>user\n", + "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?<|im_end|>\n", + "<|im_start|>assistant\n", + "Natalia sold 48/2 = <<48/2=24>>24 clips in May.\n", + "Natalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\n", + "#### 72<|im_end|>\n", + "<|im_start|>assistant\n", + "\n" + ] + } + ], + "source": [ + "ds = load_dataset(\"openai/gsm8k\", \"main\")\n", + "\n", + "\n", + "def process_dataset(sample):\n", + " messages = [\n", + " {\"role\": \"user\", \"content\": sample.get(\"question\", \"\")},\n", + " {\"role\": \"assistant\",\"content\": sample.get(\"answer\", \"\")},\n", + " ]\n", + " sample = {'messages': messages}\n", + " return sample\n", + "\n", + "ds = ds.map(process_dataset)\n", + "\n", + "print(ds)\n", + "train_sample = ds[\"train\"][0][\"messages\"]\n", + "print(train_sample)\n", + "test_sample = ds[\"test\"][0][\"messages\"]\n", + "print(test_sample)\n", + "\n", + "input_text = tokenizer.apply_chat_template(\n", + " train_sample, tokenize=True, add_generation_prompt=True\n", + ")\n", + "\n", + "print(\"Train conversation decoded:\", tokenizer.decode(token_ids=input_text))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qlXCuRKotFlu" + }, + "source": [ + "## Conclusion\n", + "\n", + "This notebook demonstrated how to apply chat templates to different models, `SmolLM2`. By structuring interactions with chat templates, we can ensure that AI models provide consistent and contextually relevant responses.\n", + "\n", + "In the exercise you tried out converting a dataset into chatml format. 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fine-tune the `HuggingFaceTB/SmolLM2-135M` model using the `SFTTrainer` from the `trl` library. The notebook cells run and will finetune the model. You can select your difficulty by trying out different datasets.\n", + "\n", + "
\n", + "

Exercise: Fine-Tuning SmolLM2 with SFTTrainer

\n", + "

Take a dataset from the Hugging Face hub and finetune a model on it.

\n", + "

Difficulty Levels

\n", + "

๐Ÿข Use the `HuggingFaceTB/smoltalk` dataset

\n", + "

๐Ÿ• Try out the `bigcode/the-stack-smol` dataset and finetune a code generation model on a specific subset `data/python`.

\n", + "

๐Ÿฆ Select a dataset that relates to a real world use case your interested in

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "62f26eb3104b46a6b685a2a2d38748d2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='
\n", + " \n", + " \n", + " [1000/1000 31:34, Epoch 1/2]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
StepTraining LossValidation Loss
501.0715001.158980
1001.1176001.124061
1501.0683001.095477
2001.0537001.079689
2501.0467001.070453
3001.0347001.061464
3501.0088001.054747
4001.0121001.050790
4501.0266001.042635
5001.0817001.033717
5500.9961001.028264
6000.8062001.033085
6500.8166001.029896
7000.7581001.031148
7500.8606001.028455
8000.8147001.024913
8500.7809001.025295
9000.8281001.022838
9500.8669001.021673
10000.7959001.021487

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Train the model\n", + "trainer.train()\n", + "\n", + "# Save the model\n", + "trainer.save_model(f\"./{finetune_name}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "28b7f1c65053456797f5797b22b904eb", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "events.out.tfevents.1733423038.Cano.34232.0: 0%| | 0.00/32.4k [00:00\n", + "

Bonus Exercise: Generate with fine-tuned model

\n", + "

๐Ÿ• Use the fine-tuned to model generate a response, just like with the base example..

\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "user\n", + "I'm looking for a beach resort for my next vacation. Can you recommend some popular ones?\n", + "assistant\n", + "I'd recommend visiting the Maldives, a small island nation in the Indian Ocean. They have a beautiful beach and a charming culture. You can also consider visiting the Maldives' sister island, Sri Lanka. Both offer a great\n" + ] + } + ], + "source": [ + "# Test the fine-tuned model on the same prompt\n", + "fine_tuned_model_path = \"./sft_output/checkpoint-1000/\"\n", + "fine_tuned_model = AutoModelForCausalLM.from_pretrained(fine_tuned_model_path).to(device)\n", + "tokenizer = AutoTokenizer.from_pretrained(fine_tuned_model_path)\n", + "\n", + "# Let's test the base model before training\n", + "prompt = \"I'm looking for a beach resort for my next vacation. Can you recommend some popular ones?\"\n", + "\n", + "# Format with template\n", + "messages = [{\"role\": \"user\", \"content\": prompt}]\n", + "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n", + "\n", + "# Generate response\n", + "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n", + "\n", + "# TODO: use the fine-tuned to model generate a response, just like with the base example.\n", + "\n", + "output = fine_tuned_model.generate(\n", + " inputs[\"input_ids\"],\n", + " max_new_tokens=50, \n", + " temperature=0.7, \n", + " top_k=50,\n", + " top_p=0.9\n", + ")\n", + "\n", + "response = tokenizer.decode(output[0], skip_special_tokens=True)\n", + "print(response)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ๐Ÿ’ You're done!\n", + "\n", + "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolLM2-135M` model using the `SFTTrainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n", + "\n", + "- Try this notebook on a harder difficulty\n", + "- Review a colleagues PR\n", + "- Improve the course material via an Issue or PR." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/1_instruction_tuning/student_examples/oztrkoguz/chat_templates_example.ipynb b/1_instruction_tuning/student_examples/oztrkoguz/chat_templates_example.ipynb new file mode 100644 index 00000000..825167fe --- /dev/null +++ b/1_instruction_tuning/student_examples/oztrkoguz/chat_templates_example.ipynb @@ -0,0 +1,411 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "F3nzKE2UiC5Y" + }, + "source": [ + "# Exploring Chat Templates with SmolLM2 and Llama 3.2\n", + "\n", + "This notebook demonstrates how to use chat templates with the `SmolLM2` and `Llama 3.2` models. Chat templates help structure interactions between users and AI models, ensuring consistent and contextually appropriate responses." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "RQyZSF2fiC5Z", + "outputId": "2f4825e1-d177-4117-feca-2f714491cdeb" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\smol_course\\env\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "# Import necessary libraries\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "from trl import setup_chat_format\n", + "import torch" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cl3KwX0JiC5a" + }, + "source": [ + "## SmolLM2 Chat Template\n", + "\n", + "Let's explore how to use a chat template with the `SmolLM2` model. We'll define a simple conversation and apply the chat template." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "A0-6k26niC5a" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\smol_course\\env\\lib\\site-packages\\huggingface_hub\\file_download.py:139: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\admin\\.cache\\huggingface\\hub\\models--HuggingFaceTB--SmolLM2-135M. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n", + "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n", + " warnings.warn(message)\n" + ] + } + ], + "source": [ + "# Dynamically set the device\n", + "device = \"cuda\" if torch.cuda.is_available() else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n", + "\n", + "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n", + "model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=model_name).to(device)\n", + "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n", + "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "z8ur2jxKiC5a" + }, + "outputs": [], + "source": [ + "# Define messages for SmolLM2\n", + "messages = [\n", + " {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": \"I'm doing well, thank you! How can I assist you today?\",\n", + " },\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HZHBCpqDiC5a" + }, + "source": [ + "# Apply chat template without tokenization\n", + "\n", + "The tokenizer represents the conversation as a string with special tokens to describe the role of the user and the assistant.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "Bc38j3kriC5b", + "outputId": "f826dc68-3052-421e-b45d-4e40515a3068" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conversation with template: <|im_start|>user\n", + "Hello, how are you?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm doing well, thank you! How can I assist you today?<|im_end|>\n", + "\n" + ] + } + ], + "source": [ + "input_text = tokenizer.apply_chat_template(messages, tokenize=False)\n", + "\n", + "print(\"Conversation with template:\", input_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "o2sOIqSEiC5b" + }, + "source": [ + "# Decode the conversation\n", + "\n", + "Note that the conversation is represented as above but with a further assistant message.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "5cSaq3_XiC5b", + "outputId": "fa92bd64-820b-4941-b71f-730d9f20b09e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conversation decoded: <|im_start|>user\n", + "Hello, how are you?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm doing well, thank you! How can I assist you today?<|im_end|>\n", + "<|im_start|>assistant\n", + "\n" + ] + } + ], + "source": [ + "input_text = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True)\n", + "\n", + "print(\"Conversation decoded:\", tokenizer.decode(token_ids=input_text))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Y-0OaQZ9iC5b" + }, + "source": [ + "# Tokenize the conversation\n", + "\n", + "Of course, the tokenizer also tokenizes the conversation and special token as ids that relate to the model's vocabulary.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "cnNislvxiC5b", + "outputId": "1d51e564-036e-44ea-e2b3-a1c136a383ef" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conversation tokenized: [1, 4093, 198, 19556, 28, 638, 359, 346, 47, 2, 198, 1, 520, 9531, 198, 57, 5248, 2567, 876, 28, 9984, 346, 17, 1073, 416, 339, 4237, 346, 1834, 47, 2, 198, 1, 520, 9531, 198]\n" + ] + } + ], + "source": [ + "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)\n", + "\n", + "print(\"Conversation tokenized:\", input_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "AgMBIAZliC5b" + }, + "source": [ + "
\n", + "

Exercise: Process a dataset for SFT

\n", + "

Take a dataset from the Hugging Face hub and process it for SFT.

\n", + "

Difficulty Levels

\n", + "

๐Ÿข Convert the `HuggingFaceTB/smoltalk` dataset into chatml format.

\n", + "

๐Ÿ• Convert the `openai/gsm8k` dataset into chatml format.

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "3eAAyOtdiC5b", + "outputId": "797bebd3-d1fe-492a-87e1-f3670d7bb64d" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\admin\\AppData\\Local\\Temp\\ipykernel_17336\\1790625692.py:1: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython.display\n", + " from IPython.core.display import display, HTML\n" + ] + }, + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.core.display import display, HTML\n", + "\n", + "display(\n", + " HTML(\"\"\"\n", + "\"\"\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "PK5VV_4yiC5c", + "outputId": "2916e3a4-d6f6-475b-bb37-95980d13a11a" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\smol_course\\env\\lib\\site-packages\\huggingface_hub\\file_download.py:139: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\admin\\.cache\\huggingface\\hub\\datasets--HuggingFaceTB--smoltalk. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n", + "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n", + " warnings.warn(message)\n", + "Generating train split: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 2260/2260 [00:00<00:00, 216137.15 examples/s]\n", + "Generating test split: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 119/119 [00:00<00:00, 59653.66 examples/s]\n", + "Map: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 2260/2260 [00:00<00:00, 27975.89 examples/s]\n", + "Map: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 119/119 [00:00<00:00, 6164.13 examples/s]\n" + ] + } + ], + "source": [ + "from datasets import load_dataset\n", + "\n", + "ds = load_dataset(\"HuggingFaceTB/smoltalk\", \"everyday-conversations\")\n", + "\n", + "\n", + "def process_dataset(sample):\n", + " # TODO: ๐Ÿข Convert the sample into a chat format\n", + " return sample\n", + "\n", + "\n", + "ds = ds.map(process_dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "30VHtxjaiC5c", + "outputId": "52af2644-eaeb-4915-ddfa-7e1ee717ac98" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\n", + " HTML(\"\"\"\n", + "\"\"\")\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "OEivfg05iC5c" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\smol_course\\env\\lib\\site-packages\\huggingface_hub\\file_download.py:139: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\admin\\.cache\\huggingface\\hub\\datasets--openai--gsm8k. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n", + "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n", + " warnings.warn(message)\n", + "Generating train split: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 7473/7473 [00:00<00:00, 748871.91 examples/s]\n", + "Generating test split: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 1319/1319 [00:00<00:00, 264778.74 examples/s]\n", + "Map: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 7473/7473 [00:00<00:00, 41621.07 examples/s]\n", + "Map: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 1319/1319 [00:00<00:00, 8758.23 examples/s]\n" + ] + } + ], + "source": [ + "ds = load_dataset(\"openai/gsm8k\", \"main\")\n", + "\n", + "\n", + "def process_dataset(sample):\n", + " # TODO: ๐Ÿ• Convert the sample into a chat format\n", + " return sample\n", + "\n", + "\n", + "ds = ds.map(process_dataset)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dNRKj14YiC5c" + }, + "source": [ + "## Conclusion\n", + "\n", + "This notebook demonstrated how to apply chat templates to different models, `SmolLM2`. By structuring interactions with chat templates, we can ensure that AI models provide consistent and contextually relevant responses.\n", + "\n", + "In the exercise you tried out converting a dataset into chatml format. Luckily, TRL will do this for you, but it's useful to understand what's going on under the hood." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.0" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/1_instruction_tuning/student_examples/oztrkoguz/sft_finetuning_example.ipynb b/1_instruction_tuning/student_examples/oztrkoguz/sft_finetuning_example.ipynb new file mode 100644 index 00000000..13c72794 --- /dev/null +++ b/1_instruction_tuning/student_examples/oztrkoguz/sft_finetuning_example.ipynb @@ -0,0 +1,178 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Supervised Fine-Tuning with SFTTrainer\n", + "\n", + "This notebook demonstrates how to fine-tune the `HuggingFaceTB/SmolLM2-135M` model using the `SFTTrainer` from the `trl` library. The notebook cells run and will finetune the model. You can select your difficulty by trying out different datasets.\n", + "\n", + "
\n", + "

Exercise: Fine-Tuning SmolLM2 with SFTTrainer

\n", + "

Take a dataset from the Hugging Face hub and finetune a model on it.

\n", + "

Difficulty Levels

\n", + "

๐Ÿข Use the `HuggingFaceTB/smoltalk` dataset

\n", + "

๐Ÿ• Try out the `bigcode/the-stack-smol` dataset and finetune a code generation model on a specific subset `data/python`.

\n", + "

๐Ÿฆ Select a dataset that relates to a real world use case your interested in

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "from datasets import load_dataset\n", + "from trl import SFTConfig, SFTTrainer, setup_chat_format\n", + "\n", + "# Load the model and tokenizer\n", + "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n", + "model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path=model_name).to(\"mps\")\n", + "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n", + "\n", + "# Set up the chat format\n", + "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset Preparation\n", + "\n", + "We will load a sample dataset and format it for training. The dataset should be structured with input-output pairs, where each input is a prompt and the output is the expected response from the model." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generating train split: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 2260/2260 [00:00<00:00, 217017.95 examples/s]\n", + "Generating test split: 100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 119/119 [00:00<00:00, 59292.25 examples/s]\n" + ] + } + ], + "source": [ + "# Load a sample dataset\n", + "from datasets import load_dataset\n", + "\n", + "# TODO: define your dataset and config using the path and name parameters\n", + "ds = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: ๐Ÿฆ If your dataset is not in a 'chatml' format, you will need to process it. Refer to the [module](../chat_templates.md)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configuring the SFTTrainer\n", + "\n", + "The `SFTTrainer` is configured with various parameters that control the training process. These include the number of training steps, batch size, learning rate, and evaluation strategy. Adjust these parameters based on your specific requirements and computational resources." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Configure the SFTTrainer\n", + "sft_config = SFTConfig(\n", + " output_dir=\"./sft_output\",\n", + " max_steps=1000, # Adjust based on dataset size and desired training duration\n", + " per_device_train_batch_size=4, # Set according to your GPU memory capacity\n", + " learning_rate=5e-5, # Common starting point for fine-tuning\n", + " logging_steps=10, # Frequency of logging training metrics\n", + " save_steps=100, # Frequency of saving model checkpoints\n", + " evaluation_strategy=\"steps\", # Evaluate the model at regular intervals\n", + " eval_steps=50, # Frequency of evaluation\n", + " use_mps_device=True,\n", + ")\n", + "\n", + "# Initialize the SFTTrainer\n", + "trainer = SFTTrainer(\n", + " model=model,\n", + " args=sft_config,\n", + " train_dataset=ds[\"train\"],\n", + " tokenizer=tokenizer,\n", + " eval_dataset=ds[\"test\"],\n", + ")\n", + "\n", + "# TODO: ๐Ÿฆ ๐Ÿ• align the SFTTrainer params with your chosen dataset. For example, if you are using the `bigcode/the-stack-smol` dataset, you will need to choose the `content` column`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Training the Model\n", + "\n", + "With the trainer configured, we can now proceed to train the model. The training process will involve iterating over the dataset, computing the loss, and updating the model's parameters to minimize this loss." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Train the model\n", + "trainer.train()\n", + "\n", + "# Save the model\n", + "trainer.save_model(\"./sft_output\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ๐Ÿ’ You're done!\n", + "\n", + "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolLM2-135M` model using the `SFTTrainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n", + "\n", + "- Try this notebook on a harder difficulty\n", + "- Review a colleagues PR\n", + "- Improve the course material via an Issue or PR." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/1_instruction_tuning/student_examples/vksx/chat_templates.ipynb b/1_instruction_tuning/student_examples/vksx/chat_templates.ipynb new file mode 100644 index 00000000..ed2259ce --- /dev/null +++ b/1_instruction_tuning/student_examples/vksx/chat_templates.ipynb @@ -0,0 +1,5883 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "vZAvFVIAtFlq" + }, + "source": [ + "# Exploring Chat Templates with SmolLM2\n", + "\n", + "This notebook demonstrates how to use chat templates with the `SmolLM2` model. Chat templates help structure interactions between users and AI models, ensuring consistent and contextually appropriate responses." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "from dotenv import load_dotenv\n", + "\n", + "load_dotenv()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "K-lZu8JvtwUN", + "outputId": "c3871418-15bc-4265-ae8d-6d6036036d0e" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n" + ] + } + ], + "source": [ + "# Install the requirements in Google Colab\n", + "# !pip install transformers datasets trl huggingface_hub\n", + "\n", + "# Authenticate to Hugging Face\n", + "from huggingface_hub import login\n", + "\n", + "login(token=os.getenv(\"HF_TOKEN\"))\n", + "\n", + "# for convenience you can create an environment variable containing your hub token as HF_TOKEN" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "tnHzBR7vtFlr" + }, + "outputs": [], + "source": [ + "# Import necessary libraries\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "from trl import setup_chat_format\n", + "import torch" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XTVOqbuetFlr" + }, + "source": [ + "## SmolLM2 Chat Template\n", + "\n", + "Let's explore how to use a chat template with the `SmolLM2` model. We'll define a simple conversation and apply the chat template." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 397, + "referenced_widgets": [ + "b922b90106414644bc0e933f28dea1bf", + "e0a40f83ae2e4ab29376a1d48b53aa6e", + "547eeb64ffd34e509c0b8b8ba6d657e2", + "45675fb5f5c94f8cae575582f7ae41a7", + "016d5e929f1240cea067372b2191d107", + "a026a32dd6d646bea82c1ebb06147d89", + "0479fd3fc1ba476ab46f8c0a98f89468", + "cbc312cb858b48a5a0f8dbcf60b7e684", + "f70401b6dba74380b19bd1ef887b3bf7", + "7eb91920e4384194a008902d6c4a09c7", + "b379da78cb34463aa5a72eedc3d176cd", + "ae2690497e024095adb3879643cffd33", + "f600aa1fe4094133888ec9a2504a60eb", + "efe9a9fcebfe441b80075fbfe9c32674", + "50dbf8861ca94b0ba1f4a7e2f0d8aead", + "547151540399460fb9a946bbe67afbd9", + "5291041c86db4933816088c047d659d8", + "48724ba7ba4e4f00923445245640739f", + "04ae3f7b640c42f3a8eb1977cd1a585d", + "db3bd55d779947028f36a8b24a2621b6", + "d17c62b889754b5d88cfced5b18ff7a7", + "990f706db474450ba0997d1dbcd53cb7", + "3b881514716c47308061fe85b810a6a4", + "26ed0f1bae204d74a313d101d9355e90", + "4ff5af1784904bc9b85515105885e2d8", + "b3c42d7e25d6494993029531adc3866d", + "6227b40396ea4024b3c8710c5e65601f", + "7612cc9b8908471b90c9118151d6e447", + "b687aca79e6e470b96254c5e309d6d63", + "3fa18e3b50104af796bd0887f556224a", + "4bfa3103048a47989a09a0d90ac6b9bf", + "85de66e1ee3140cf85eadebe5fea1e9f", + "b31de9bcf83e4070be09c7d663361232", + "d64d50101891491f96ff80162dc6d26c", + "d65ec0f0dc0b44e0869c6159e6e82ad6", + "76febcd912404a58add3a39f80a8218d", + "f4ea276bdc0d4da2a04b46e3f1ed95b5", + "0942430d36de4677b4c2fa771d7bcd2a", + "10a0f37020d44156a11e9750778892e0", + "58fb913274b54a60a832513c09608a2f", + "0bab42beb845475684e9e71dd1591e1d", + "89ecd1b28ab64c90afe3b9736fd48306", + "be4e145938054f13a510fe4d04a7a60d", + "648c3c820b39493daf0cce5f57a55467", + "01e0f8a799ad479eb95eef3e5a09bd70", + "8fe2df9a14a0436c9124a856ac7419e4", + "d108e029e743419989e30f64f0c82b90", + "bfd11f21f197459b8f27ef364bc9b264", + "76a0341ebe9f4c3face32460d7023be9", + "da1a999fb5af4eae9f6a9d1086cbb4cf", + "77f6c27c3c854138b4aa9789637141a1", + "6ceb292f2b8544f2a9a005d16d3e8978", + "41a27cf0a91246599d4d1b7dae7c7863", + "745fb1db425e44e5b3a23b36ae7675d1", + "bde95b39561145548fc81fb4cc94a1bf", + "3cc519fd92fe4b328943ec839115b63e", + "e15fc503bb73476980cedb5f06b51ced", + "d8c5dc8df3be4e65b2bbba020d29150f", + "c0177c4ad18740d88acfc603ce4735f8", + "eb570fd159124e2cbd2df9335b3f9cd6", + "5de5dab3d92f4f41838a8f302d27f0c3", + "471b481a3e5b4d439ab31fdc49fc99c7", + "7a0c705334694da6b750104b28db6dba", + "0c336ea5c653434da49e2f0e949f83d0", + "ec15d99b3a604405a2b4707931d4bf44", + "e7f5d507d9564941bb7db742b4bf01c7", + "aa2d32cb76ba47ebaa5ea391efbf58a7", + "7b20c7c8f6be40c6815b8531ecb9c936", + "e90b58981bd34d0e8f975fc1a9658c4c", + "5b7b09d983844f7893bdda411f9a0076", + "70f0eaed6ef14c2db8aecb592edeb1ad", + "d32017fa83aa44f6b2e3443a602654be", + "ff8debfb713f4b88be6b9b3bf33bfca2", + "ed577dea3ac54884a637ad775b42bc68", + "d43410dfcc8c4bebb8672f10ed2aeb66", + "0206fb9662a349c1aa8a6d87ce01c388", + "881b6196dfa0446e8c55a2420e484b6b", + "d54fb2da9f1f4a89ae962b8816314f43", + "77d3d81687e6417ab988b04984fc68f4", + "fbce0a69847e4099a55d1e39d4118c91", + "1513792bad534a0c9c381a131395c519", + "69f38fecf8ad403898634cfdfadf8925", + "17023310de9b4c3ebd8cc03758d59ef9", + "f3e23f781bce4429954d76bfea97aff4", + "530fc4c2bf1244628af7dea3e4b35cdf", + "96c2aae9198441569362135ad4bcbc98", + "76d306c21214412ab44e542d82e547aa", + "b9e41ef9e9c54fa7b71bc333604af74e" + ] + }, + "id": "Nrxh0oX6tFls", + "outputId": "953e1527-8168-4346-9338-6e188ca31a1a" + }, + "outputs": [], + "source": [ + "# Dynamically set the device\n", + "device = (\n", + " \"cuda\"\n", + " if torch.cuda.is_available()\n", + " else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n", + ")\n", + "\n", + "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " pretrained_model_name_or_path=model_name\n", + ").to(device)\n", + "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n", + "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "zkJwILrbtFls" + }, + "outputs": [], + "source": [ + "# Define messages for SmolLM2\n", + "messages = [\n", + " {\"role\": \"user\", \"content\": \"Hello, how are you?\"},\n", + " {\n", + " \"role\": \"assistant\",\n", + " \"content\": \"I'm doing well, thank you! How can I assist you today?\",\n", + " },\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ve4dgtjstFls" + }, + "source": [ + "# Apply chat template without tokenization\n", + "\n", + "The tokenizer represents the conversation as a string with special tokens to describe the role of the user and the assistant.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pbAg-5x-tFls", + "outputId": "5f9482db-1fcf-4c13-ccaa-ef3f6eff7f76" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conversation with template: <|im_start|>user\n", + "Hello, how are you?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm doing well, thank you! How can I assist you today?<|im_end|>\n", + "\n" + ] + } + ], + "source": [ + "input_text = tokenizer.apply_chat_template(messages, tokenize=False)\n", + "\n", + "print(\"Conversation with template:\", input_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "sfvdglOqtFls" + }, + "source": [ + "# Decode the conversation\n", + "\n", + "Note that the conversation is represented as above but with a further assistant message.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "mXUVdPeytFls", + "outputId": "80870e53-7bc1-426e-ac33-ba6748e030fc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conversation decoded: <|im_start|>user\n", + "Hello, how are you?<|im_end|>\n", + "<|im_start|>assistant\n", + "I'm doing well, thank you! How can I assist you today?<|im_end|>\n", + "<|im_start|>assistant\n", + "\n" + ] + } + ], + "source": [ + "input_text = tokenizer.apply_chat_template(\n", + " messages, tokenize=True, add_generation_prompt=True\n", + ")\n", + "\n", + "print(\"Conversation decoded:\", tokenizer.decode(token_ids=input_text))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UcZQpspEtFlt" + }, + "source": [ + "# Tokenize the conversation\n", + "\n", + "Of course, the tokenizer also tokenizes the conversation and special token as ids that relate to the model's vocabulary.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jc2PLxAMtFlt", + "outputId": "d2098780-b3f4-41ec-a1f3-b6da2b593c62" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Conversation tokenized: [1, 4093, 198, 19556, 28, 638, 359, 346, 47, 2, 198, 1, 520, 9531, 198, 57, 5248, 2567, 876, 28, 9984, 346, 17, 1073, 416, 339, 4237, 346, 1834, 47, 2, 198, 1, 520, 9531, 198]\n" + ] + } + ], + "source": [ + "input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True)\n", + "\n", + "print(\"Conversation tokenized:\", input_text)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m3eNp9a0tFlt" + }, + "source": [ + "
\n", + "

Exercise: Process a dataset for SFT

\n", + "

Take a dataset from the Hugging Face hub and process it for SFT.

\n", + "

Difficulty Levels

\n", + "

๐Ÿข Convert the `HuggingFaceTB/smoltalk` dataset into chatml format.

\n", + "

๐Ÿ• Convert the `openai/gsm8k` dataset into chatml format.

\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 381 + }, + "id": "qbkXV2_ItFlt", + "outputId": "06deadc3-2c63-4660-d2bd-05096ef07c9f" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/_8/qn2k4xt16ld4cf0bbsd75z7c0000gn/T/ipykernel_41602/4068687702.py:1: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython.display\n", + " from IPython.core.display import display, HTML\n" + ] + }, + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.core.display import display, HTML\n", + "\n", + "display(\n", + " HTML(\n", + " \"\"\"\n", + "\"\"\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241, + "referenced_widgets": [ + "c2d74a42fb574b8892d0a288fd92f0a6", + "056b9ef5706843b19cd62fce75743afb", + "17b4d81e40564a53bb79be9fbef4918e", + "951f60cddcb84dfdbbdf2058369f0541", + "646484cf7a36444daebe1dfe4a0e4150", + "e2f0c39ce1c046e8acb150dfbfaf5aa8", + "7eb12d70d2b542a7b651c7680f590279", + "ea1f9cb22abf4e7d9f6e76fc86c03387", + "00c9f5ca71b84df4b26acee72c97fefb", + "505f96bc0c7843bcb1498ba1c1ba5f06", + "635cc2881a1e4b8788bb26c356740e04", + "a6ee323c13904525a99c6f092ba96e18", + "67fffe7d6f8c4963972b408529e05532", + "0055b6b628934affaf88bc58a1572bb6", + "aafbbb9fc5164fa3a88193bfd33d2f79", + "606e39d53ed64967a60337418c71c595", + "26b15fa18b1b4963a1ba76a76675e7ee", + "db09ab1f79db4f3a8de77f0348eca0f7", + "de04f344a8d4428e8ba1836a563d8aa1", + "03c09673186046d799d6f487d6623e6b", + "1cc682330b24431b8812c73041e987d0", + "dafe748a452148038779f6a62a22a4ec", + "addad1c100024c44a0959978153da9a8", + "9bea2a23db644ad19b708d10e35d54ee", + "d1174b127571420593971166fbb1966b", + "add90ed3746d4293a1b71198137a892c", + "8def25e6389f4e6192b517b6e80aa05e", + "c9747e7a810f413ba1ea108307e3ad1d", + "d0ea49d1d90f4d34bf2ae70efa96946e", + "59d0997b85614384bbfebeee928340b6", + "269920491c134501873e0110367bc984", + "384d26051c04460e8870a3ffe9406c48", + "8e8a0e89a50646c897e546c4077db79e", + "ff60308921f9432683acbcd6d29fb78f", + "3bc8f6339f4e4a3b961d810255c5573e", + "4780ad263ec04b1a97525d985e102049", + "488feef55878426bbf1c753c6d58735b", + "560ba45d70ca431dadeb327d234c330a", + "04d0a6f74af346f7bc696951949063c8", + "2a18ce941b0f4cef8307988ef898b47f", + "194e3fda3635466b998f96e3dc22746a", + "e2ab3cb38b5a41f68d18ed5f0e6ae22c", + "f0b271bcac6c43a9aaddac54259bb514", + "0dc93d50a283472f9ca64fd0a4c6ff15", + "dd1a50d4497144388a1809b78bb38f58", + "6b72a856e5bd4812a5e0dd0c3bfb8455", + "4e21a567d1f6461985727823b37166e1", + "ec1efb7598fd496bb170673ae1b8a1df", + "84f393468aa74baa903243d238b2d387", + "a54ce365be104d27aaa15cf8c63b5ebe", + "1791220377d141ac9b307246177d0712", + "fa330d4f0fb241aebd065f6ef4a6892c", + "cfa1cc6eed8a4f7791a7959308456b6b", + "b50c9c4433854cf7a6b2593e946b7faa", + "7557cd24ba9b4aa3955866d59db94519", + "cc608dfb880c49d4bc5acf2d691b8ec6", + "cb838c5bed994a9a8e6fcf5c98b76d17", + "76bbe8c2beba4c0594085d32a68d2ee7", + "c9836c952b07472880649b82e2347e8d", + "383db57f997140d482b82b123080837a", + "182abc7ec4d944d9bb2ec1281c98b4c8", + "6934c6d1cbac44dbb08f3fffe3056edb", + "05fa0f6eb78b4c56b219b0e57521bd2e", + "012aa94e3cf24e32833c6bbca23c52f7", + "76c1a1cdc9054bbe90d0d3b662cf0ed1", + "e453f1672772400a851735ba64f42c8b", + "d1358f6b16644cb3a2328ca639a4a77a", + "c19f60d4028045399c62004027eaafd9", + "8055588a1fa940239c801ef66f3ecf3b", + "7468a9bc8bda44e5b44574c64fdc6803", + "a13a8f8b702e44ed88c7d358a0a8b4b4", + "13367fbb763747fa8de94cde40ffae32", + "b1fcf477db664ccdade4096fb79de327", + "9d1c06ac6b774d82adca58773f389161", + "31910159cf30463b8246ec47ffd8ab5b", + "72220420f9d340eabec13a01caebc92c", + "55b14c03a41c495aacf8ac2d0f96ba0b" + ] + }, + "id": "4p3atw4_tFlu", + "outputId": "62ee9812-3819-4a9c-9e24-5687368ffcd8" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Formatted Chat:\n", + " <|im_start|>user\n", + "Hi there<|im_end|>\n", + "<|im_start|>assistant\n", + "Hello! How can I help you today?<|im_end|>\n", + "<|im_start|>user\n", + "I'm looking for a beach resort for my next vacation. Can you recommend some popular ones?<|im_end|>\n", + "<|im_start|>assistant\n", + "Some popular beach resorts include Maui in Hawaii, the Maldives, and the Bahamas. They're known for their beautiful beaches and crystal-clear waters.<|im_end|>\n", + "<|im_start|>user\n", + "That sounds great. Are there any resorts in the Caribbean that are good for families?<|im_end|>\n", + "<|im_start|>assistant\n", + "Yes, the Turks and Caicos Islands and Barbados are excellent choices for family-friendly resorts in the Caribbean. They offer a range of activities and amenities suitable for all ages.<|im_end|>\n", + "<|im_start|>user\n", + "Okay, I'll look into those. Thanks for the recommendations!<|im_end|>\n", + "<|im_start|>assistant\n", + "You're welcome. I hope you find the perfect resort for your vacation.<|im_end|>\n", + "\n" + ] + } + ], + "source": [ + "from datasets import load_dataset\n", + "\n", + "ds_smoltalk = load_dataset(\"HuggingFaceTB/smoltalk\", \"everyday-conversations\")\n", + "\n", + "\n", + "def process_dataset(sample):\n", + " # TODO: ๐Ÿข Convert the sample into a chat format\n", + " # The dataset is already in chat format\n", + "\n", + " # use the tokenizer's method to apply the chat template\n", + " sample[\"formatted_chat\"] = tokenizer.apply_chat_template(sample[\"messages\"])\n", + " return sample\n", + "\n", + "\n", + "ds_smoltalk = ds_smoltalk.map(process_dataset)\n", + "print(\n", + " \"\\nFormatted Chat:\\n\",\n", + " tokenizer.decode(token_ids=ds_smoltalk[\"train\"][\"formatted_chat\"][0]),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 381 + }, + "id": "81fQeazltFlu", + "outputId": "36cf7148-9881-4f13-d0ce-76c82c4ab219" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "display(\n", + " HTML(\n", + " \"\"\"\n", + "\"\"\"\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true, + "id": "bWUSv7NMtFlu" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset:\n", + " DatasetDict({\n", + " train: Dataset({\n", + " features: ['question', 'answer'],\n", + " num_rows: 7473\n", + " })\n", + " test: Dataset({\n", + " features: ['question', 'answer'],\n", + " num_rows: 1319\n", + " })\n", + "})\n", + "\n", + "Chat:\n", + " {'question': 'Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?', 'answer': 'Natalia sold 48/2 = <<48/2=24>>24 clips in May.\\nNatalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\\n#### 72'}\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "82e26b01401e4dd391249ab58e86e0ad", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/7473 [00:00user\n", + "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?<|im_end|>\n", + "<|im_start|>assistant\n", + "Natalia sold 48/2 = <<48/2=24>>24 clips in May.\n", + "Natalia sold 48+24 = <<48+24=72>>72 clips altogether in April and May.\n", + "#### 72<|im_end|>\n", + "\n" + ] + } + ], + "source": [ + "ds_gsm8k = load_dataset(\"openai/gsm8k\", \"main\")\n", + "print(\"Dataset:\\n\", ds_gsm8k)\n", + "print(\"\\nChat:\\n\", ds_gsm8k[\"train\"][0])\n", + "\n", + "\n", + "def process_dataset(sample):\n", + " # TODO: ๐Ÿ• Convert the sample into a chat format\n", + "\n", + " # 1. create a message format with the role and content\n", + " messages = [\n", + " {\"content\": sample[\"question\"], \"role\": \"user\"},\n", + " {\"content\": sample[\"answer\"], \"role\": \"assistant\"},\n", + " ]\n", + " sample = {\"messages\": messages}\n", + "\n", + " # 2. apply the chat template to the samples using the tokenizer's method\n", + " sample[\"formatted_chat\"] = tokenizer.apply_chat_template(sample[\"messages\"])\n", + " return sample\n", + "\n", + "\n", + "ds_gsm8k = ds_gsm8k.map(process_dataset)\n", + "print(\n", + " \"\\nTokenized Chat:\\n\",\n", + " tokenizer.decode(token_ids=ds_gsm8k[\"train\"][\"formatted_chat\"][0]),\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qlXCuRKotFlu" + }, + "source": [ + "## Conclusion\n", + "\n", + "This notebook demonstrated how to apply chat templates to different models, `SmolLM2`. By structuring interactions with chat templates, we can ensure that AI models provide consistent and contextually relevant responses.\n", + "\n", + "In the exercise you tried out converting a dataset into chatml format. Luckily, TRL will do this for you, but it's useful to understand what's going on under the hood." + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "0055b6b628934affaf88bc58a1572bb6": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "FloatProgressModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "FloatProgressModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", 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The notebook cells run and will finetune the model. You can select your difficulty by trying out different datasets.\n", + "\n", + "
\n", + "

Exercise: Fine-Tuning SmolLM2 with SFTTrainer

\n", + "

Take a dataset from the Hugging Face hub and finetune a model on it.

\n", + "

Difficulty Levels

\n", + "

๐Ÿข Use the `HuggingFaceTB/smoltalk` dataset

\n", + "

๐Ÿ• Try out the `bigcode/the-stack-smol` dataset and finetune a code generation model on a specific subset `data/python`.

\n", + "

๐Ÿฆ Select a dataset that relates to a real world use case your interested in

\n", + "
" + ] + }, + { + "cell_type": "code", + "source": [ + "from google.colab import userdata\n", + "access_token = userdata.get('HF_TOKEN')" + ], + "metadata": { + "id": "B87DNGxWhpcF" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "83LTJiL4bgjv" + }, + "outputs": [], + "source": [ + "# Install the requirements in Google Colab\n", + "# !pip install transformers datasets trl huggingface_hub\n", + "\n", + "# Authenticate to Hugging Face\n", + "\n", + "from huggingface_hub import login\n", + "login(token=access_token)\n", + "\n", + "# for convenience you can create an environment variable containing your hub token as HF_TOKEN" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "IGLHJ_MObgjw", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 241, + "referenced_widgets": [ + "299839e423c543dfb4cc017c115ce9bf", + "4fb079ce9b05415e9ae7adeb806d6a30", + 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(Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n", + "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ยทยทยทยทยทยทยทยทยทยท\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "Tracking run with wandb version 0.18.7" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "Run data is saved locally in /content/wandb/run-20241206_222954-9yxl5zs9" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "Syncing run ./sft_output to Weights & Biases (docs)
" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + " View project at https://wandb.ai/vikas-sharma-exl/huggingface" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + " View run at https://wandb.ai/vikas-sharma-exl/huggingface/runs/9yxl5zs9" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
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StepTraining LossValidation Loss
501.0657001.158981
1001.1116001.124065
1501.0624001.095485
2001.0482001.079699
2501.0412001.070457
3001.0292001.061473
3501.0034001.054751
4001.0065001.050794
4501.0211001.042637
5001.0762001.033726
5500.9907001.028269
6000.8018001.033090
6500.8120001.029896
7000.7538001.031147
7500.8559001.028454
8000.8103001.024910
8500.7769001.025294
9000.8237001.022836
9500.8623001.021671
10000.7915001.021485

" + ] + }, + "metadata": {} + } + ], + "source": [ + "# Train the model\n", + "trainer.train()\n", + "\n", + "# Save the model\n", + "trainer.save_model(f\"./{finetune_name}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "id": "-fUQfzh1bgjy", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 216, + "referenced_widgets": [ + "667c31db2cd24c1c8f1b1b656172d784", + "6eea19b15d9a46d4a63338cc906aa2fa", + "45c53aefe68f47d09938cddc92832b89", + "51b8b750a7684f5f96110a0b5fafcd25", + "7c3eb04624754d429dbfab2773259fb6", + "c9a915095574427e9277b1a303083142", + "781ec67c10d8475d92f58d7ac7f0fdf9", + "080e866923f14772ad7de47edc085743", + "fbd0513e3cd04f20ac04b1955ad15d09", + "7d793afb78914190a5eb73f885308aa9", + "5cf7cf67bc394e4595da6f404ced34cb", + "f0eb6e13ea2944828ed83f426ae2fa00", + "e9278714bb67475696abd2e60225e6b4", + "26469fc949124bfd8fe65747a664dbb1", + "ed5f61b984774254b2b69b24ce13e0d8", + "1577b1fb47c347f693313b0e0970f575", + "d170a6874ca74152b6e3a91736864a3d", + "0d435d4f175b4fa2b4b7cb85b8344a0e", + "0628b1c976e04ab1bac7555cfcc26a81", + "e53584a78def43b49984c64b408889de", + "866a47201b6e4bf09540355b1cbfdf3e", + "0181cab51c5949938f114bc0c9559408", + "41751a13d1684ca38996b48dfd248f8f", + "b483f25c5b0e4d45b3f16fe67f32b308", + "fc7b76fca1f34029b03230f35e0032ae", + "c0fa46a5cd9648fe8519c2ddcdaa2062", + "c70cf250178f41f2b48780fafbf8d412", + "ab2e413065ca431c8ccc4350a983bc2f", + "aebc2744b70847218568c64e260a2033", + "9dacffa7c1c74d859c3d253411035b1c", + "0c505afa0ed54621bbedca71aed1bc11", + "844e27ee7044466d88a6c0fa3105d395", + "33e8727d38bb4e2088b10d8dc869d550", + "6733273077ce453997156e1346f9a6ef", + "b5f7088931f6441ba5bc0dafe068ff13", + "82094a3132624ad9bc9e45e4c61a7a04", + "db569615078e4350b659edeb1d3bd622", + "ccb7f92d0dba4ba28a4b9aedccc5c1da", + "f10bb9512f2046b585b58789969f1e72", + "e5f3da700b5948369a71b832b77477df", + "b61e740a780644cfb00a3429284d2fd6", + "20ddeba40aaa4095bde45218b2c1ee26", + "9f41e0985ff84a5d8cfa75d3b6234a82", + "b39d9f65a30749409cfbe408282d3e81" + ] + }, + "outputId": "dcd2e607-ead3-4f0a-c8d9-e2a86e58ff25" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "model.safetensors: 0%| | 0.00/538M [00:00\n", + "

Bonus Exercise: Generate with fine-tuned model

\n", + "

๐Ÿ• Use the fine-tuned to model generate a response, just like with the base example..

\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "id": "3Yx0Qm-Obgjz", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b48c88dd-a01c-4fd7-b2ef-3acbf8500cab" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "After training:\n", + "user\n", + "Write a haiku about programming\n", + "assistant\n", + "Hello! How can I help you today? I'm going to write a haiku about programming. What's programming? It's a type of art where you create a sequence of steps to solve a problem. Think of it like a recipe. You follow the steps, and then you see the result. Can you think of any programming languages? Python, Java, or JavaScript are all popular ones. What's the most popular programming language right now? Python is very popular,\n" + ] + } + ], + "source": [ + "# Test the fine-tuned model on the same prompt\n", + "\n", + "# Let's test the base model before training\n", + "prompt = \"Write a haiku about programming\"\n", + "\n", + "# Format with template\n", + "messages = [{\"role\": \"user\", \"content\": prompt}]\n", + "formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)\n", + "\n", + "# Generate response\n", + "inputs = tokenizer(formatted_prompt, return_tensors=\"pt\").to(device)\n", + "\n", + "# TODO: use the fine-tuned to model generate a response, just like with the base example.\n", + "trainer.model.eval()\n", + "outputs = trainer.model.generate(**inputs, max_new_tokens=100)\n", + "print(\"After training:\")\n", + "print(tokenizer.decode(outputs[0], skip_special_tokens=True))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hUkO9jrKbgjz" + }, + "source": [ + "## ๐Ÿ’ You're done!\n", + "\n", + "This notebook provided a step-by-step guide to fine-tuning the `HuggingFaceTB/SmolLM2-135M` model using the `SFTTrainer`. By following these steps, you can adapt the model to perform specific tasks more effectively. If you want to carry on working on this course, here are steps you could try out:\n", + "\n", + "- Try this notebook on a harder difficulty\n", + "- Review a colleagues PR\n", + "- Improve the course material via an Issue or PR." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + }, + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "accelerator": "GPU", + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "299839e423c543dfb4cc017c115ce9bf": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": 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combined length of prompt + response in tokens\n", - " max_length=1536,\n", + " #max_length=1536,\n", ")" ] }, @@ -357,7 +357,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.10" + "version": "3.12.7" }, "widgets": { "application/vnd.jupyter.widget-state+json": { diff --git a/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb b/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb index bd83ab6b..e7303b29 100644 --- a/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb +++ b/2_preference_alignment/notebooks/orpo_finetuning_example.ipynb @@ -9,7 +9,7 @@ "This notebook will guide you through the process of fine-tuning a language model using Odds Ratio Preference Optimization (ORPO). We will use the SmolLM2-135M model which has **not** been through SFT training, so it is not compatible with DPO. This means, you cannot use the model you trained in [1_instruction_tuning](../../1_instruction_tuning/notebooks/sft_finetuning_example.ipynb).\n", "\n", "
\n", - "

Exercise: Aligning SmolLM2 with DPOTrainer

\n", + "

Exercise: Aligning SmolLM2 with ORPOTrainer

\n", "

Take a dataset from the Hugging Face hub and align a model on it.

\n", "

Difficulty Levels

\n", "

๐Ÿข Use the `trl-lib/ultrafeedback_binarized` dataset

\n", @@ -271,7 +271,7 @@ "model, tokenizer = setup_chat_format(model, tokenizer)\n", "\n", "# Set our name for the finetune to be saved &/ uploaded to\n", - "finetune_name = \"SmolLM2-FT-DPO\"\n", + "finetune_name = \"SmolLM2-FT-ORPO\"\n", "finetune_tags = [\"smol-course\", \"module_1\"]" ] }, diff --git a/2_preference_alignment/orpo.md b/2_preference_alignment/orpo.md index 5983eaab..29ede4eb 100644 --- a/2_preference_alignment/orpo.md +++ b/2_preference_alignment/orpo.md @@ -76,7 +76,7 @@ Key parameters to consider: ## Next Steps -โฉ Try the [ORPO Tutorial](./notebooks/orpo_tutorial.ipynb) to implement this unified approach to preference alignment. +โฉ Try the [ORPO Tutorial](./notebooks/orpo_finetuning_example.ipynb) to implement this unified approach to preference alignment. ## Resources - [ORPO Paper](https://arxiv.org/abs/2403.07691) diff --git a/3_parameter_efficient_finetuning/README.md b/3_parameter_efficient_finetuning/README.md index cd88ad6f..7dabe6a8 100644 --- a/3_parameter_efficient_finetuning/README.md +++ b/3_parameter_efficient_finetuning/README.md @@ -26,7 +26,7 @@ Prompt tuning offers an **even lighter** approach by **adding trainable tokens t | Title | Description | Exercise | Link | Colab | |-------|-------------|----------|------|-------| | LoRA Fine-tuning | Learn how to fine-tune models using LoRA adapters | ๐Ÿข Train a model using LoRA
๐Ÿ• Experiment with different rank values
๐Ÿฆ Compare performance with full fine-tuning | [Notebook](./notebooks/finetune_sft_peft.ipynb) | Open In Colab | -| Load LoRA Adapters | Learn how to load and use trained LoRA adapters | ๐Ÿข Load pre-trained adapters
๐Ÿ• Merge adapters with base model
๐Ÿฆ Switch between multiple adapters | [Notebook](./notebooks/load_lora_adapter_example.ipynb) | Open In Colab | +| Load LoRA Adapters | Learn how to load and use trained LoRA adapters | ๐Ÿข Load pre-trained adapters
๐Ÿ• Merge adapters with base model
๐Ÿฆ Switch between multiple adapters | [Notebook](./notebooks/load_lora_adapter.ipynb) | Open In Colab | ## Resources @@ -36,4 +36,4 @@ Prompt tuning offers an **even lighter** approach by **adding trainable tokens t - [Prompt Tuning Paper](https://arxiv.org/abs/2104.08691) - [Hugging Face PEFT Guide](https://huggingface.co/blog/peft) - [How to Fine-Tune LLMs in 2024 with Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) -- [TRL](https://huggingface.co/docs/trl/index) \ No newline at end of file +- [TRL](https://huggingface.co/docs/trl/index) diff --git a/5_vision_language_models/README.md b/5_vision_language_models/README.md index 73c88a46..48d95fed 100644 --- a/5_vision_language_models/README.md +++ b/5_vision_language_models/README.md @@ -24,12 +24,14 @@ For detailed guidance on fine-tuning VLMs, visit the [VLM Fine-Tuning](./vlm_fin | Title | Description | Exercise | Link | Colab | |-------|-------------|----------|------|-------| -| VLM Usage | Learn how to load and use a pre-trained VLM for various tasks | ๐Ÿข Process an image
๐Ÿ• Process multiple images with batch handling
๐Ÿฆ Process a full video| [Notebook](./notebooks/vlm_usage_sample.ipynb) | Open In Colab | -| VLM Fine-Tuning | Learn how to fine-tune a pre-trained VLM for task-specific datasets | ๐Ÿข Use a basic dataset for fine-tuning
๐Ÿ• Try a new dataset
๐Ÿฆ Experiment with alternative fine-tuning methods | [Notebook](./notebooks/vlm_finetune_sample.ipynb)| Open In Colab | +| VLM Usage | Learn how to load and use a pre-trained VLM for various tasks | ๐Ÿข Process an image
๐Ÿ• Process multiple images with batch handling
๐Ÿฆ Process a full video| [Notebook](./notebooks/vlm_usage_sample.ipynb) | Open In Colab | +| VLM Fine-Tuning | Learn how to fine-tune a pre-trained VLM for task-specific datasets | ๐Ÿข Use a basic dataset for fine-tuning
๐Ÿ• Try a new dataset
๐Ÿฆ Experiment with alternative fine-tuning methods | [Notebook](./notebooks/vlm_sft_sample.ipynb)| Open In Colab | ## References -- [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl) +- [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl) +- [Hugging Face Learn: Supervised Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl) +- [Hugging Face Learn: Preference Optimization Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct) - [Hugging Face Blog: Preference Optimization for VLMs](https://huggingface.co/blog/dpo_vlm) - [Hugging Face Blog: Vision Language Models](https://huggingface.co/blog/vlms) - [Hugging Face Blog: SmolVLM](https://huggingface.co/blog/smolvlm) diff --git a/5_vision_language_models/notebooks/vlm_dpo_sample.ipynb b/5_vision_language_models/notebooks/vlm_dpo_sample.ipynb deleted file mode 100644 index e69de29b..00000000 diff --git a/5_vision_language_models/notebooks/vlm_sft_sample.ipynb b/5_vision_language_models/notebooks/vlm_sft_sample.ipynb index afd78a0f..b37bc534 100644 --- a/5_vision_language_models/notebooks/vlm_sft_sample.ipynb +++ b/5_vision_language_models/notebooks/vlm_sft_sample.ipynb @@ -12,9 +12,9 @@ "

Exercise: Fine-Tuning SmolVLM with SFTTrainer

\n", "

Take a dataset from the Hugging Face hub and finetune a model on it.

\n", "

Difficulty Levels

\n", - "

๐Ÿข Use the `...` dataset with supervised method.

\n", - "

๐Ÿ• Try preference optimization.

\n", - "

๐Ÿฆ Try out the other datasets, other methods.

\n", + "

๐Ÿข Use the `\"HuggingFaceM4/ChartQA\"` dataset for SFT training run.

\n", + "

๐Ÿ• Use the fine-tuned to model generate a response, and improve upon the base example..

\n", + "

๐Ÿฆ Try out the other datasets and show improvement.

\n", "
" ] }, @@ -447,16 +447,6 @@ " trainer.push_to_hub(tags=finetune_tags)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "

Bonus Exercise: Generate with fine-tuned model

\n", - "

๐Ÿ• Use the fine-tuned to model generate a response, just like with the base example..

\n", - "
" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/5_vision_language_models/vlm_finetuning.md b/5_vision_language_models/vlm_finetuning.md index 0dd69ad1..90ed8f76 100644 --- a/5_vision_language_models/vlm_finetuning.md +++ b/5_vision_language_models/vlm_finetuning.md @@ -92,7 +92,9 @@ Preference datasets must capture clear preferences between candidate outputs. Fo ## Resources -- [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl) +- [Hugging Face Learn: Supervised Fine-Tuning VLMs](https://huggingface.co/learn/cookbook/fine_tuning_vlm_trl) +- [Hugging Face Learn: Supervised Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_smol_vlm_sft_trl) +- [Hugging Face Learn: Preference Optimization Fine-Tuning SmolVLM](https://huggingface.co/learn/cookbook/fine_tuning_vlm_dpo_smolvlm_instruct) - [Hugging Face Blog: Preference Optimization for VLMs](https://huggingface.co/blog/dpo_vlm) ## Next Steps diff --git a/5_vision_language_models/vlm_usage.md b/5_vision_language_models/vlm_usage.md index def75e5a..ef812927 100644 --- a/5_vision_language_models/vlm_usage.md +++ b/5_vision_language_models/vlm_usage.md @@ -6,11 +6,11 @@ Visual Language Models (VLMs) bridge the gap between images and text, enabling a VLMs combine image-processing components with text-generation models to achieve a unified understanding. The primary elements of their architecture are: -![VLM Architecture](VLM_Architecture.png) +![VLM Architecture](./images/VLM_Architecture.png) - **Image Encoder**: Transforms raw images into compact numerical representations. Pretrained encoders such as CLIP or vision transformers (ViT) are commonly used. - **Embedding Projector**: Maps image features into a space compatible with textual embeddings, often using dense layers or linear transformations. -- **Text Decoder**: Acts as the language-generation component, translating fused multimodal information into coherent text. Examples include generative models like LLaMA or Vicuna. +- **Text Decoder**: Acts as the language-generation component, translating fused multimodal information into coherent text. Examples include generative models like Llama or Vicuna. - **Multimodal Projector**: Provides an additional layer to blend image and text representations. It is critical for models like LLaVA to establish stronger connections between the two modalities. Most VLMs leverage pretrained image encoders and text decoders and align them through additional fine-tuning on paired image-text datasets. This approach makes training efficient while allowing the models to generalize effectively. diff --git a/6_synthetic_datasets/README.md b/6_synthetic_datasets/README.md index be0ef659..f14d4d00 100644 --- a/6_synthetic_datasets/README.md +++ b/6_synthetic_datasets/README.md @@ -1,3 +1,39 @@ # Synthetic Datasets -I'm still working on this section... \ No newline at end of file +Synthetic data is artificially generated data that mimics real-world usage. It allows overcoming data limitations by expanding or enhancing datasets. Even though synthetic data was already used for some usescases, large language models have made synthetic datasets more popular for pre- and post-training, and the evaluation of language models. + +We'll use [`distilabel`](https://distilabel.argilla.io/latest/), a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers. For a deeper dive into the package and best practices, check out the [documentation](https://distilabel.argilla.io/latest/). + +## Module Overview + +Synthetic data for language models can be categorized into three taxonomies: instructions, preferences and critiques. We will focus on the first two categories, which focus on the generation of datasets for instruction tuning and preference alignment. In both categories, we will cover aspects of the third category, which focuses on improving existing data with model critiques and rewrites. + +![Synthetic Data Taxonomies](./images/taxonomy-synthetic-data.png) + +## Contents + +### 1. [Instruction Datasets](./instruction_datasets.md) + +Learn how to generate instruction datasets for instruction tuning. We will explore creating instruction tuning datasets thorugh basic prompting and using prompts more refined techniques from papers. Instruction tuning datasets with seed data for in-context learning can be created through methods like SelfInstruct and Magpie. Additionally, we will explore instruction evolution through EvolInstruct. [Start learning](./instruction_datasets.md). + +### 2. [Preference Datasets](./preference_datasets.md) + +Learn how to generate preference datasets for preference alignment. We will build on top of the methods and techniques introduced in section 1, by generating additional responses. Next, we will learn how to improve such responses with the EvolQuality prompt. Finally, we will explore how to evaluate responses with the the UltraFeedback prompt which will produce a score and critique, allowing us to create preference pairs. [Start learning](./preference_datasets.md). + +### Exercise Notebooks + +| Title | Description | Exercise | Link | Colab | +|-------|-------------|----------|------|-------| +| Instruction Dataset | Generate a dataset for instruction tuning | ๐Ÿข Generate an instruction tuning dataset
๐Ÿ• Generate a dataset for instruction tuning with seed data
๐Ÿฆ Generate a dataset for instruction tuning with seed data and with instruction evolution | [Link](./notebooks/instruction_sft_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb) | +| Preference Dataset | Generate a dataset for preference alignment | ๐Ÿข Generate a preference alignment dataset
๐Ÿ• Generate a preference alignment dataset with response evolution
๐Ÿฆ Generate a preference alignment dataset with response evolution and critiques | [Link](./notebooks/preference_alignment_dataset.ipynb) | [Colab](https://githubtocolab.com/huggingface/smol-course/tree/main/6_synthetic_datasets/notebooks/preference_alignment_dataset.ipynb) | + +## Resources + +- [Distilabel Documentation](https://distilabel.argilla.io/latest/) +- [Synthetic Data Generator is UI app](https://huggingface.co/blog/synthetic-data-generator) +- [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smoltalk) +- [Self-instruct](https://arxiv.org/abs/2212.10560) +- [Evol-Instruct](https://arxiv.org/abs/2304.12244) +- [Magpie](https://arxiv.org/abs/2406.08464) +- [UltraFeedback](https://arxiv.org/abs/2310.01377) +- [Deita](https://arxiv.org/abs/2312.15685) diff --git a/6_synthetic_datasets/images/pipeline.png b/6_synthetic_datasets/images/pipeline.png new file mode 100644 index 00000000..d7f11764 Binary files /dev/null and b/6_synthetic_datasets/images/pipeline.png differ diff --git a/6_synthetic_datasets/images/taxonomy-synthetic-data.png b/6_synthetic_datasets/images/taxonomy-synthetic-data.png new file mode 100644 index 00000000..d764e8ce Binary files /dev/null and b/6_synthetic_datasets/images/taxonomy-synthetic-data.png differ diff --git a/6_synthetic_datasets/instruction_datasets.md b/6_synthetic_datasets/instruction_datasets.md new file mode 100644 index 00000000..c909ee64 --- /dev/null +++ b/6_synthetic_datasets/instruction_datasets.md @@ -0,0 +1,204 @@ +# Generating Instruction Datasets + +Within [the chapter on instruction tuning](../1_instruction_tuning/README.md), we learned about fine-tuning models with Supervised Fine-tuning. In this section, we will explore how to generate instruction datasets for SFT. We will explore creating instruction tuning datasets through basic prompting and using more refined techniques from papers. Instruction tuning datasets with seed data for in-context learning can be created through methods like SelfInstruct and Magpie. Additionally, we will explore instruction evolution through EvolInstruct. Lastly, we will explore how to generate a dataset for instruction tuning using a distilabel pipeline. + +## From Prompt to Data + +Synthetic data sounds fancy, but it can be simplified as creating data through effective prompting to extract knowledge from a model. In turn, you can think of this as a way to generate data for a specific task. The challenge is prompting effectively while ensuring the data is diverse and representative. Fortunately, many papers have explored this problem, and we will explore some of the useful ones during this course. First things first, we will explore how to generate synthetic data through manual prompting. + +### Basic Prompting + +Let's start with a basic example and load the [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) model using the `transformers` integration of the `distilabel` library. We will use the `TextGeneration` class to generate a synthetic `prompt` and use that to generate a `completion`. + +Next, we will load the model using the `distilabel` library. + +```python +from distilabel.llms import TransformersLLM +from distilabel.steps.tasks import TextGeneration + +llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct") +gen = TextGeneration(llm=llm) +gen.load() +``` + +!!! note + Distilabel loads the `llm` into memory, so, when working in a notebook, we need to `gen.unload()` after we are done with it to avoid memory issues. + +We will now use the `llm` to generate a `prompt` for instruction tuning. + +```python +next(gen.process([{"instruction": "Generate a questions about the Hugging Face Smol-Course on small AI models."}])) +# What is the purpose of Smol-Course? +``` + +Lastly, we can use that same `prompt` as input to generate a `completion`. + +```python +next(gen.process([{"instruction": "What is the purpose of Smol-Course?"}])) +# The Smol-Course is a platform designed to learning computer science concepts. +``` + +Cool! We can generated a synthetic `prompt` and a corresponding `completion`. Re-using this simple approach at scale will allow us to generate a lot more data however, the quality of the data is not that great and does not take into account the nuances of our course or domain. Additionally, re-running the current code shows us the data is not that diverse. Luckily, there are ways to solve this problem. + +### SelfInstruct + +SelfInstruct is a prompt that generates new instructions based on a seed dataset. This seed data can be a single instruction or a piece of context. The process begins with a pool of initial seed data. The language model is then prompted to generate new instructions based on this seed data using in-context learning. The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/blob/main/src/distilabel/steps/tasks/templates/self-instruct.jinja2) and a simplified version is shown below: + +``` +# Task Description +Develop {{ num_instructions }} user queries that can be received by the given AI application and applicable to the provided context. Emphasize diversity in verbs and linguistic structures within the model's textual capabilities. + +# Context +{{ input }} + +# Output +``` + +To use it, we need to pass the `llm` to the [SelfInstruct class](https://distilabel.argilla.io/dev/components-gallery/tasks/selfinstruct/). Let's use the text from the [Prompt to Data section](#prompt-to-data) as context and generate a new instruction. + +```python +from distilabel.steps.tasks import SelfInstruct + +self_instruct = SelfInstruct(llm=llm) +self_instruct.load() + +context = "" + +next(self_instruct.process([{"input": text}]))["instructions"][0] +# What is the process of generating synthetic data through manual prompting? +``` + +The generated instruction is a lot better already and it fits our actual content and domain. However, we can do even better by improving the prompt through evolution. + +### EvolInstruct + +EvolInstruct is a prompting technique that takes an input instruction and evolves it into a better version of the same instruction. This better version is defined according to a set of criteria and adds constraints, deepening, concretizing, reasoning or complications to the original instruction. The process can be repeated multiple times to create various evolutions of the same instruction, ideally leading to a better version of the original instruction. The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_instruct) and a simplified version is shown below: + +``` +I want you act as a Prompt Rewriter. +Given prompt a prompt, rewrite it into a more complex version. +Complicate the prompt based on the following criteria: +{{ criteria }} + +# Prompt +{{ input }} + +# Output +``` + +To use it, we need to pass the `llm` to the [EvolInstruct class](https://distilabel.argilla.io/dev/components-gallery/tasks/evolinstruct/). Let's use the synthetic prompt from [the SelfInstruct section](#selfinstruct) as input and evolve it into a better version. For this example, we will only evolve for one generation. + +```python +from distilabel.steps.tasks import EvolInstruct + +evol_instruct = EvolInstruct(llm=llm, num_evolutions=1) +evol_instruct.load() + +text = "What is the process of generating synthetic data through manual prompting" + +next(evol_instruct.process([{"instruction": text}])) +# What is the process of generating synthetic data through manual prompting? +# And, how does the artificial intelligence system, GPT4, use machine learning algorithms to manipulate the input data into synthetic data? +``` + +The instruction is now more complex but has lost some of the original meaning. So, take into account that evolving can be a double-edged sword and we need to be careful with the quality of the data we generate. + +### Magpie + +Magpie is a technique that relies on the auto-regressive factors of language model and the [chat-template](../1_instruction_tuning/chat_templates.md) that has been using during the instruction tuning process. As you might remember, the chat-template is a format that structures conversations with clear role indicators (system, user, assistant). During the instruction tuning phase, the language model has been optimized to reproduce this format and that is exactly what magpie takes advantage of. It starts with a pre-query-prompt based on the chat-template but it stops before the user message indicator, e.g. `<|im_start|>user\n`, and then it uses the language model to generate the user prompt until the end of the assistant indicator, e.g. `<|im_end|>`. This approach allows us to generate a lot of data in a very efficient way and it can even be scaled up to multi-turn conversations. It is hypothesized this generated data reproduces training data from the instruction tuning phase of the model used. + +In this scenario, prompt templates differ per model because they are based on the chat-template format. But we can walk through a simplified version of the process step-by-step. + +```bash +# Step 1: provide the pre-query-prompt +<|im_start|>user\n + +# Step 2: the language model generates the user prompt +<|im_start|>user\n +What is the purpose of Smol-Course? + +# Step 3: stop the generation +<|im_end|> +``` + +To use it in distilabel, we need to pass the `llm` to the [Magpie class](https://distilabel.argilla.io/dev/components-gallery/tasks/magpie/). + +```python +from distilabel.steps.tasks import Magpie + +magpie = Magpie(llm=llm) +magpie.load() + +next(magpie.process([{"system_prompt": "You are a helpful assistant."}])) +# [{ +# "role": "user", +# "content": "Can you provide me with a list of the top 3 universities?" +# }, +# { +# "role": "assistant", +# "content": "The top 3 universities are: MIT, Yale, Stanford." +# }] +``` + + +We immediately get a dataset with a `prompt` and `completion` . To improve the performance on our own domain, we can inject additional context into the `system_prompt`. For the LLM to generate specific domain data in combination with Magpie, it helps describing in the system prompt what the users queries will be. This is then used in the pre-query-prompt before we start generating the user prompt and bias the LLM to generate user queries of that domain. + +``` +You're an AI assistant that will help users solving math problems. +``` + +It's important to write the system prompt as shown above instead of something like: + +``` +You're an AI assistant that generates math problems +``` + +Generally, language models are less optimized for passing additional context to the `system_prompt` so this does not always work as well for customisation as other techniques. + +### From Prompts to Pipelines + +The classes we've seen so far are all standalone classes that can be used in a pipeline. This is a good start, but we can do even better by using the `Pipeline` class to generate a dataset. We will use the `TextGeneration` step to generate a synthetic dataset for instruction tuning. The pipeline will consist of a `LoadDataFromDicts` step to load the data, a `TextGeneration` step to generate the `prompt` and a `completion` for that prompt. We will connect the steps and flow the data through the pipeline using the `>>` operator. Within the [documentation of distilabel](https://distilabel.argilla.io/dev/components-gallery/tasks/textgeneration/#input-output-columns) we can see input and output columns of the step. We to ensure that the data flow correctly through the pipeline, we will use the `output_mappings` parameter to map the output columns to the input columns of the next step. + +```python +from distilabel.llms import TransformersLLM +from distilabel.pipeline import Pipeline +from distilabel.steps import LoadDataFromDicts +from distilabel.steps.tasks import TextGeneration + +with Pipeline() as pipeline: + data = LoadDataFromDicts(data=[{"instruction": "Generate a short question about the Hugging Face Smol-Course."}]) + llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct") + gen_a = TextGeneration(llm=llm, output_mappings={"generation": "instruction"}) + gen_b = TextGeneration(llm=llm, output_mappings={"generation": "response"}) + data >> gen_a >> gen_b + +if __name__ == "__main__": + distiset = pipeline.run(use_cache=False) + print(distiset["default"]["train"][0]) +# [{ +# "instruction": "What is the purpose of Smol-Course?", +# "response": "The Smol-Course is a platform designed to learning computer science concepts." +# }] +``` + +Under the hood, this pipeline has a lot of cool features. It automatically caches generation results, so we can don't have to re-run the generation steps. There is included fault-tolerance, so if the generation steps fail, the pipeline will continue to run. And the pipeline exexutes all generation steps in parallel, so the generation is faster. We can even visualise the pipeline using the `draw` method. Here you can see how the data flows through the pipeline and how the `output_mappings` are used to map the output columns to the input columns of the next step. + +![Pipeline](./images/pipeline.png) + +## Best Practices + +- Ensure you have a diverse seed data to cover a wide range of scenarios +- Regularly evaluate the dataset to ensure generated data is diverse and of high quality +- Iterate on the (system)prompt to improve the quality of the data + +## Next Steps + +๐Ÿ‘จ๐Ÿฝโ€๐Ÿ’ป Code -[Exercise Notebook](./notebooks/instruction_sft_dataset.ipynb) to generate a dataset for instruction tuning +๐Ÿง‘โ€๐Ÿซ Learn - About [generating preference datasets](./preference_datasets.md) + +## References + +- [Distilabel Documentation](https://distilabel.argilla.io/latest/) +- [Self-instruct](https://arxiv.org/abs/2212.10560) +- [Evol-Instruct](https://arxiv.org/abs/2304.12244) +- [Magpie](https://arxiv.org/abs/2406.08464) \ No newline at end of file diff --git a/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb b/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb new file mode 100644 index 00000000..54584722 --- /dev/null +++ b/6_synthetic_datasets/notebooks/instruction_sft_dataset.ipynb @@ -0,0 +1,119 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generate a dataset for instruction tuning\n", + "\n", + "This notebook will guide you through the process of generating a dataset for instruction tuning. We'll use the `distilabel` package to generate a dataset for instruction tuning.\n", + "\n", + "So let's dig in to some instruction tuning datasets.\n", + "\n", + "
\n", + "

Exercise: Generate a dataset for instruction tuning

\n", + "

Now that you've seen how to generate a dataset for instruction tuning, try generating a dataset for instruction tuning.

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Difficulty Levels

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๐Ÿข Generate an instruction tuning dataset

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๐Ÿ• Generate a dataset for instruction tuning with seed data

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๐Ÿฆ Generate a dataset for instruction tuning with seed data and with instruction evolution

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" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "## Install dependencies\n", + "\n", + "Instead of transformers, you can also install `vllm` or `hf-inference-endpoints`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install \"distilabel[hf-transformers,outlines,instructor]\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Start synthesizing\n", + "\n", + "As we've seen in the previous course content, we can create a distilabel pipelines for instruction dataset generation. The bare minimum pipline is already provided. Make sure to scale up this pipeline to generate a large dataset for instruction tuning. Swap out models, model providers and generation arguments to see how they affect the quality of the dataset. Experiment small, scale up later.\n", + "\n", + "Check out the [distilabel components gallery](https://distilabel.argilla.io/latest/components-gallery/) for information about the processing classes and how to use them. \n", + "\n", + "An example of loading data from the Hub instead of dictionaries is provided below.\n", + "\n", + "```python\n", + "from datasets import load_dataset\n", + "\n", + "with Pipeline(...) as pipeline:\n", + " ...\n", + "\n", + "if __name__ == \"__main__:\n", + " dataset = load_dataset(\"my-dataset\", split=\"train\")\n", + " distiset = pipeline.run(dataset=dataset)\n", + "```\n", + "\n", + "Don't forget to push your dataset to the Hub after running the pipeline!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from distilabel.llms import TransformersLLM\n", + "from distilabel.pipeline import Pipeline\n", + "from distilabel.steps import LoadDataFromDicts\n", + "from distilabel.steps.tasks import TextGeneration\n", + "\n", + "with Pipeline() as pipeline:\n", + " data = LoadDataFromDicts(data=[{\"instruction\": \"Generate a short question about the Hugging Face Smol-Course.\"}])\n", + " llm = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n", + " gen_a = TextGeneration(llm=llm, output_mappings={\"generation\": \"instruction\"})\n", + " gen_b = TextGeneration(llm=llm, output_mappings={\"generation\": \"response\"})\n", + " data >> gen_a >> gen_b\n", + "\n", + "if __name__ == \"__main__\":\n", + " distiset = pipeline.run(use_cache=False)\n", + " distiset.push_to_hub(\"huggingface-smol-course-instruction-tuning-dataset\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ๐ŸŒฏ That's a wrap\n", + "\n", + "You've now seen how to generate a dataset for instruction tuning. You could use this to:\n", + "\n", + "- Generate a dataset for instruction tuning.\n", + "- Create evaluation datasets for instruction tuning.\n", + "\n", + "Next\n", + "\n", + "๐Ÿง‘โ€๐Ÿซ Learn - About [generating preference datasets](./preference_datasets.md)\n", + "๐Ÿ‹๏ธโ€โ™‚๏ธ Fine-tune a model for instruction tuning with a synthetic dataset based on the [instruction tuning chapter](../../1_instruction_tuning/README.md)\n" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb b/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb new file mode 100644 index 00000000..cd9747df --- /dev/null +++ b/6_synthetic_datasets/notebooks/preference_dpo_dataset.ipynb @@ -0,0 +1,120 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generate a dataset for preference alignment\n", + "\n", + "This notebook will guide you through the process of generating a dataset for preference alignment. We'll use the `distilabel` package to generate a dataset for preference alignment.\n", + "\n", + "So let's dig in to some preference alignment datasets.\n", + "\n", + "
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Exercise: Generate a dataset for preference alignment

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Now that you've seen how to generate a dataset for preference alignment, try generating a dataset for preference alignment.

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Difficulty Levels

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๐Ÿข Generate a dataset for preference alignment

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๐Ÿ• Generate a dataset for preference alignment with response evolution

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๐Ÿฆ Generate a dataset for preference alignment with response evolution and model pooling

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" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "vscode": { + "languageId": "plaintext" + } + }, + "source": [ + "## Install dependencies\n", + "\n", + "Instead of transformers, you can also install `vllm` or `hf-inference-endpoints`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install \"distilabel[hf-transformers,outlines,instructor]\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Start synthesizing\n", + "\n", + "As we've seen in the previous notebook, we can create a distilabel pipeline for preference dataset generation. The bare minimum pipline is already provided. You can continue work on this pipeline to generate a large dataset for preference alignment. Swap out models, model providers and generation arguments to see how they affect the quality of the dataset. Experiment small, scale up later.\n", + "\n", + "Check out the [distilabel components gallery](https://distilabel.argilla.io/latest/components-gallery/) for information about the processing classes and how to use them. \n", + "\n", + "An example of loading data from the Hub instead of dictionaries is provided below.\n", + "\n", + "```python\n", + "from datasets import load_dataset\n", + "\n", + "with Pipeline(...) as pipeline:\n", + " ...\n", + "\n", + "if __name__ == \"__main__:\n", + " dataset = load_dataset(\"my-dataset\", split=\"train\")\n", + " distiset = pipeline.run(dataset=dataset)\n", + "```\n", + "\n", + "Don't forget to push your dataset to the Hub after running the pipeline!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from distilabel.llms import TransformersLLM\n", + "from distilabel.pipeline import Pipeline\n", + "from distilabel.steps import GroupColumns, LoadDataFromDicts\n", + "from distilabel.steps.tasks import TextGeneration\n", + "\n", + "with Pipeline() as pipeline:\n", + " data = LoadDataFromDicts(data=[{\"instruction\": \"What is synthetic data?\"}])\n", + " llm_a = TransformersLLM(model=\"HuggingFaceTB/SmolLM2-1.7B-Instruct\")\n", + " gen_a = TextGeneration(llm=llm_a)\n", + " llm_b = TransformersLLM(model=\"Qwen/Qwen2.5-1.5B-Instruct\")\n", + " gen_b = TextGeneration(llm=llm_b)\n", + " group = GroupColumns(columns=[\"generation\"])\n", + " data >> [gen_a, gen_b] >> group\n", + "\n", + "if __name__ == \"__main__\":\n", + " distiset = pipeline.run()\n", + " distiset.push_to_hub(\"huggingface-smol-course-preference-tuning-dataset\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## ๐ŸŒฏ That's a wrap\n", + "\n", + "You've now seen how to generate a dataset for preference alignment. You could use this to:\n", + "\n", + "- Generate a dataset for preference alignment.\n", + "- Create evaluation datasets for preference alignment.\n", + "\n", + "Next\n", + "\n", + "๐Ÿ‹๏ธโ€โ™‚๏ธ Fine-tune a model with preference alignment with a synthetic dataset based on the [preference tuning chapter](../../2_preference_alignment/README.md) \n" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/6_synthetic_datasets/preference_datasets.md b/6_synthetic_datasets/preference_datasets.md new file mode 100644 index 00000000..38bb0e02 --- /dev/null +++ b/6_synthetic_datasets/preference_datasets.md @@ -0,0 +1,155 @@ +# Generating Preference Datasets + +Within [the chapter on preference alignment](../2_preference_alignment/README.md), we learned about Direct Preference Optimization. In this section, we will explore how to generate preference datasets for methods like DPO. We will build on top of the methods that were introduced in [generating instruction datasets](./instruction_datasets.md). Additionally, we will show how to add extra completions to the dataset using basic prompting or by using EvolQuality to improve the quality of responses. Lastly, we will show how UltraFeedback can be used to generate scores and critiques. + +## Creating multiple completions + +Preference data is a dataset with multiple `completions` for the same `instruction`. We can add more `completions` to a dataset by prompting a model to generate them. When doing this, we need to ensure that the second completion is not too similar to the first completion in terms of overall quality and phrasing. This is important because the model needs to be optimized for a clear preference. We want to know which completion is preferred over the other, normally referred to as `chosen` and `rejected`. We will go into more detail about determining chosen and rejected completions in the [section on creating scores](#creating-scores). + +### Model pooling + +You can use models from different model families to generate a second completion, which is called model pooling. To further improve the quality of the second completion, you can use different generation arguments, like tweaking the `temperature`. Lastly, you can use different prompt templates or system prompts to generate a second completion to ensure diversity based on specific characteristics defined in the template. In theory, we could take two models of varying quality and use the better one as the `chosen` completion. + +Let's start with model pooling by loading the [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) and [HuggingFaceTB/SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct) models using the `transformers` integration of the `distilabel` library. Using these models, we will create two synthetic `responses` for a given `prompt`. We will create another pipeline with `LoadDataFromDicts`, `TextGeneration`, and `GroupColumns`. We will first load data, then use two generation steps, and then group the results. We connect the steps and flow the data through the pipeline using the `>>` operator and `[]`, which means that we want to use the output of the previous step as the input for both steps within the list. + +```python +from distilabel.llms import TransformersLLM +from distilabel.pipeline import Pipeline +from distilabel.steps import GroupColumns, LoadDataFromDicts +from distilabel.steps.tasks import TextGeneration + +with Pipeline() as pipeline: + data = LoadDataFromDicts(data=[{"instruction": "What is synthetic data?"}]) + llm_a = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct") + gen_a = TextGeneration(llm=llm_a) + llm_b = TransformersLLM(model="Qwen/Qwen2.5-1.5B-Instruct") + gen_b = TextGeneration(llm=llm_b) + group = GroupColumns(columns=["generation"]) + data >> [gen_a, gen_b] >> group + +if __name__ == "__main__": + distiset = pipeline.run() + print(distiset["default"]["train"]["grouped_generation"][0]) +# {[ +# 'Synthetic data is artificially generated data that mimics real-world usage.', +# 'Synthetic data refers to data that has been generated artificially.' +# ]} +``` + +As you can see, we have two synthetic `completions` for the given `prompt`. We could have boosted diversity by initializing the `TextGeneration` steps with a specific `system_prompt` or by passing generation arguments to the `TransformersLLM`. Let's now see how we can improve the quality of the `completions` using EvolQuality. + +### EvolQuality + +EvolQuality is similar to [EvolInstruct](./instruction_datasets.md#evolinstruct) - it is a prompting technique but it evolves `completions` instead of the input `prompt`. The task takes both a `prompt` and `completion` and evolves the `completion` into a version that better responds to the `prompt` based on a set of criteria. This better version is defined according to criteria for improving helpfulness, relevance, deepening, creativity, or details. Because this automatically generates a second completion, we can use it to add more `completions` to a dataset. In theory, we could even assume the evolution is better than the original completion and use it as the `chosen` completion out of the box. + +The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/evol_quality) and a simplified version is shown below: + +```bash +I want you act as a Response Rewriter. +Given prompt a and a response, rewrite the response into a better version. +Complicate the prompt based on the following criteria: +{{ criteria }} + +# Prompt +{{ input }} + +# Response +{{ output }} + +# Improved Response +``` + +Let's use the [EvolQuality class](https://distilabel.argilla.io/dev/components-gallery/tasks/evolquality/) to evolve the synthetic `prompt` and `completion` from [the Model Pooling section](#model-pooling) into a better version. For this example, we will only evolve for one generation. + +```python +from distilabel.llms import TransformersLLM +from distilabel.steps.tasks import EvolQuality + +llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct") +evol_quality = EvolQuality(llm=llm, num_evolutions=1) +evol_quality.load() + +instruction = "What is synthetic data?" +completion = "Synthetic data is artificially generated data that mimics real-world usage." + +next(evol_quality.process([{ + "instruction": instruction, + "response": completion +}])) +# The process of generating synthetic data through manual prompting involves creating artificial data sets that mimic real-world usage patterns. +``` + +The `response` is now more complex and specific to the `instruction`. This is a good start, but as we have seen with EvolInstruct, evolved generations are not always better. Hence, it is important to use additional evaluation techniques to ensure the quality of the dataset. We will explore this in the next section. + +## Creating Scores + +Scores are a measure of how much one response is preferred over another. In general, these scores can be absolute, subjective, or relative. For this course, we will focus on the first two because they are most valuable for creating preference datasets. This scoring is a way of judging and evaluating using language models and therefore has some overlap with the evaluation techniques we have seen in [the chapter on evaluation](../3_evaluation/README.md). As with the other evaluation techniques, scores and evaluations normally require larger models to better align with human preferences. + +### UltraFeedback + +UltraFeedback is a technique that generates scores and critiques for a given `prompt` and its `completion`. + +The scores are based on the quality of the `completion` according to a set of criteria. There are four fine-grained criteria: `helpfulness`, `relevance`, `deepening`, and `creativity`. These are useful but generally speaking, using the overall criteria is a good start, which allows us to simplify the process of generating scores. The scores can be used to determine which `completion` is the `chosen` and which is the `rejected` one. Because they are absolute, they can also be used as interesting filters for outliers in the dataset, either finding the worst completions or the pairs with more or less difference. + +The critiques are added to provide reasoning for the score. They can be used as extra context to help us understand the differences between the scores. The language model generates extensive critiques which is very useful, but this also introduces extra cost and complexity to the process because generating critiques is more expensive than generating a single token to represent a score. + +The prompt is [implemented in distilabel](https://github.com/argilla-io/distilabel/tree/main/src/distilabel/steps/tasks/templates/ultrafeedback) and a simplified version is shown below: + +```bash +Evaluate the model's outputs based on various criteria: Helpfulness, Relevance, Deepening, Creativity +Your role is to provide a holistic assessment based on the above factors. +Score the output from 1 to 5 on overall quality. + +Answer with the following format: score - rationale + +# Input +{{ input }} + +# Response +{{ output }} + +# Score - Rationale +``` + +Let's use the [UltraFeedback class](https://distilabel.argilla.io/dev/components-gallery/tasks/ultrafeedback/) to evaluate the synthetic `prompt` and `completion` from [the Model Pooling section](#model-pooling). + +```python +from distilabel.llms import TransformersLLM +from distilabel.steps.tasks import UltraFeedback + +llm = TransformersLLM(model="HuggingFaceTB/SmolLM2-1.7B-Instruct") +ultrafeedback = UltraFeedback(llm=llm) +ultrafeedback.load() + +instruction = "What is synthetic data?" +completion_a = "Synthetic data is artificially generated data that mimics real-world usage." +completion_b = "Synthetic data refers to data that has been generated artificially." + +next(ultrafeedback.process([{ + "instruction": instruction, + "generations": [completion_a, completion_b] +}])) +# [ +# { +# 'ratings': [4, 5], +# 'rationales': ['could have been more specific', 'good definition'], +# } +# ] +``` + +## Best Practices + +- Overall scores are cheaper and easier to generate than critiques and specific scores +- Use bigger models to generate scores and critiques +- Use a diverse set of models to generate scores and critiques +- Iterate on configuration of the `system_prompt` and models + +## Next Steps + +๐Ÿ‘จ๐Ÿฝโ€๐Ÿ’ป Code -[Exercise Notebook](./notebooks/preference_dpo_dataset.ipynb) to generate a dataset for instruction tuning + +## References + +- [Distilabel Documentation](https://distilabel.argilla.io/latest/) +- [Deita](https://arxiv.org/abs/2312.15685) +- [UltraFeedback](https://arxiv.org/abs/2310.01377) diff --git a/README.md b/README.md index e23ef262..363b5378 100644 --- a/README.md +++ b/README.md @@ -32,9 +32,10 @@ This course provides a practical, hands-on approach to working with small langua | [Preference Alignment](./2_preference_alignment) | Explore DPO and ORPO techniques for aligning models with human preferences | โœ… Ready | Dec 6, 2024 | | [Parameter-efficient Fine-tuning](./3_parameter_efficient_finetuning) | Learn LoRA, prompt tuning, and efficient adaptation methods | โœ… Ready | Dec 9, 2024 | | [Evaluation](./4_evaluation) | Use automatic benchmarks and create custom domain evaluations | โœ… Ready | Dec 13, 2024 | -| [Vision-language Models](./5_vision_language_models) | Adapt multimodal models for vision-language tasks | [๐Ÿšง WIP](https://github.com/huggingface/smol-course/issues/49) | Dec 16, 2024 | -| [Synthetic Datasets](./6_synthetic_datasets) | Create and validate synthetic datasets for training | [๐Ÿšง WIP](https://github.com/huggingface/smol-course/issues/83) | Dec 20, 2024 | +| [Vision-language Models](./5_vision_language_models) | Adapt multimodal models for vision-language tasks | โœ… Ready | Dec 16, 2024 | +| [Synthetic Datasets](./6_synthetic_datasets) | Create and validate synthetic datasets for training | โœ… Ready | Dec 20, 2024 | | [Inference](./7_inference) | Infer with models efficiently | ๐Ÿ“ Planned | Dec 23, 2024 | +| Capstone Project | Use what you've learnt to climb the leaderboard! | [๐Ÿšง WIP](https://github.com/huggingface/smol-course/pull/97) | Dec 23, 2024 | ## Why Small Language Models? diff --git a/ko/1_instruction_tuning/chat_templates.md b/ko/1_instruction_tuning/chat_templates.md index 6be63193..444478f2 100644 --- a/ko/1_instruction_tuning/chat_templates.md +++ b/ko/1_instruction_tuning/chat_templates.md @@ -2,13 +2,13 @@ ๋Œ€ํ™” ํ…œํ”Œ๋ฆฟ์€ ์–ธ์–ด ๋ชจ๋ธ๊ณผ ์‚ฌ์šฉ์ž ๊ฐ„ ์ƒํ˜ธ์ž‘์šฉ์„ ๊ตฌ์กฐํ™”ํ•˜๋Š” ๋ฐ ํ•„์ˆ˜์ ์ž…๋‹ˆ๋‹ค. ์ด ํ…œํ”Œ๋ฆฟ์€ ๋Œ€ํ™”์— ์ผ๊ด€๋œ ํ˜•์‹์„ ์ œ๊ณตํ•˜์—ฌ, ๋ชจ๋ธ์ด ๊ฐ ๋ฉ”์‹œ์ง€์˜ ๋งฅ๋ฝ๊ณผ ์—ญํ• ์„ ์ดํ•ดํ•˜๊ณ  ์ ์ ˆํ•œ ์‘๋‹ต ํŒจํ„ด์„ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•ฉ๋‹ˆ๋‹ค. -## ๋ฒ ์ด์Šค ๋ชจ๋ธ vs ์ง€์‹œ ๋ชจ๋ธ +## ๊ธฐ๋ณธ ๋ชจ๋ธ vs ์ง€์‹œ ๋ชจ๋ธ -๋ฒ ์ด์Šค ๋ชจ๋ธ์€ ๋‹ค์Œ ํ† ํฐ์„ ์˜ˆ์ธกํ•˜๊ธฐ ์œ„ํ•ด ๋Œ€๋Ÿ‰์˜ ์›์‹œ ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ๋กœ ํ•™์Šต๋˜๋Š” ๋ฐ˜๋ฉด, ์ง€์‹œ ๋ชจ๋ธ์€ ํŠน์ • ์ง€์‹œ๋ฅผ ๋”ฐ๋ฅด๊ณ  ๋Œ€ํ™”๋ฅผ ๋‚˜๋ˆŒ ์ˆ˜ ์žˆ๋„๋ก ๋ฏธ์„ธ ์กฐ์ •๋œ ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, `SmolLM2-135M`์€ ๋ฒ ์ด์Šค ๋ชจ๋ธ์ด๊ณ , `SmolLM2-135M-Instruct`๋Š” ์ด๋ฅผ ์ง€์‹œ ์กฐ์ •ํ•œ ๋ณ€ํ˜• ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค. +๊ธฐ๋ณธ ๋ชจ๋ธ์€ ๋‹ค์Œ ํ† ํฐ์„ ์˜ˆ์ธกํ•˜๊ธฐ ์œ„ํ•ด ๋Œ€๋Ÿ‰์˜ ์›์‹œ ํ…์ŠคํŠธ ๋ฐ์ดํ„ฐ๋กœ ํ•™์Šต๋˜๋Š” ๋ฐ˜๋ฉด, ์ง€์‹œ ๋ชจ๋ธ์€ ํŠน์ • ์ง€์‹œ๋ฅผ ๋”ฐ๋ฅด๊ณ  ๋Œ€ํ™”๋ฅผ ๋‚˜๋ˆŒ ์ˆ˜ ์žˆ๋„๋ก ๋ฏธ์„ธ ์กฐ์ •๋œ ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, `SmolLM2-135M`์€ ๊ธฐ๋ณธ ๋ชจ๋ธ์ด๊ณ , `SmolLM2-135M-Instruct`๋Š” ์ด๋ฅผ ์ง€์‹œ ์กฐ์ •ํ•œ ๋ณ€ํ˜• ๋ชจ๋ธ์ž…๋‹ˆ๋‹ค. -๋ฒ ์ด์Šค ๋ชจ๋ธ์ด ์ง€์‹œ ๋ชจ๋ธ์ฒ˜๋Ÿผ ๋™์ž‘ํ•˜๋„๋ก ๋งŒ๋“ค๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋ชจ๋ธ์ด ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ์‹์œผ๋กœ ํ”„๋กฌํ”„ํŠธ๋ฅผ ์ผ๊ด€๋˜๊ฒŒ ๋งŒ๋“ค์–ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. ์ด๋•Œ ๋Œ€ํ™” ํ…œํ”Œ๋ฆฟ์ด ์œ ์šฉํ•ฉ๋‹ˆ๋‹ค. ChatML์€ ์ด๋Ÿฌํ•œ ํ…œํ”Œ๋ฆฟ ํ˜•์‹ ์ค‘ ํ•˜๋‚˜๋กœ ๋Œ€ํ™”๋ฅผ ๊ตฌ์กฐํ™”ํ•˜๊ณ  ๋ช…ํ™•ํ•œ ์—ญํ•  ์ง€์‹œ์ž(์‹œ์Šคํ…œ, ์‚ฌ์šฉ์ž, ์–ด์‹œ์Šคํ„ดํŠธ)๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. +๊ธฐ๋ณธ ๋ชจ๋ธ์ด ์ง€์‹œ ๋ชจ๋ธ์ฒ˜๋Ÿผ ๋™์ž‘ํ•˜๋„๋ก ๋งŒ๋“ค๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋ชจ๋ธ์ด ์ดํ•ดํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉ์‹์œผ๋กœ ํ”„๋กฌํ”„ํŠธ๋ฅผ ์ผ๊ด€๋˜๊ฒŒ ๋งŒ๋“ค์–ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. ์ด๋•Œ ๋Œ€ํ™” ํ…œํ”Œ๋ฆฟ์ด ์œ ์šฉํ•ฉ๋‹ˆ๋‹ค. ChatML์€ ์ด๋Ÿฌํ•œ ํ…œํ”Œ๋ฆฟ ํ˜•์‹ ์ค‘ ํ•˜๋‚˜๋กœ ๋Œ€ํ™”๋ฅผ ๊ตฌ์กฐํ™”ํ•˜๊ณ  ๋ช…ํ™•ํ•œ ์—ญํ•  ์ง€์‹œ์ž(์‹œ์Šคํ…œ, ์‚ฌ์šฉ์ž, ์–ด์‹œ์Šคํ„ดํŠธ)๋ฅผ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. -๋ฒ ์ด์Šค ๋ชจ๋ธ์€ ์„œ๋กœ ๋‹ค๋ฅธ ๋Œ€ํ™” ํ…œํ”Œ๋ฆฟ์œผ๋กœ ๋ฏธ์„ธ ์กฐ์ •๋  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ, ์ง€์‹œ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•  ๋•Œ๋Š” ๋ฐ˜๋“œ์‹œ ํ•ด๋‹น ๋ชจ๋ธ์— ์ ํ•ฉํ•œ ๋Œ€ํ™” ํ…œํ”Œ๋ฆฟ์„ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•ฉ๋‹ˆ๋‹ค. +๊ธฐ๋ณธ ๋ชจ๋ธ์€ ์„œ๋กœ ๋‹ค๋ฅธ ๋Œ€ํ™” ํ…œํ”Œ๋ฆฟ์œผ๋กœ ๋ฏธ์„ธ ์กฐ์ •๋  ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ, ์ง€์‹œ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•  ๋•Œ๋Š” ๋ฐ˜๋“œ์‹œ ํ•ด๋‹น ๋ชจ๋ธ์— ์ ํ•ฉํ•œ ๋Œ€ํ™” ํ…œํ”Œ๋ฆฟ์„ ์‚ฌ์šฉํ•˜๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•ฉ๋‹ˆ๋‹ค. ## ๋Œ€ํ™” ํ…œํ”Œ๋ฆฟ ์ดํ•ดํ•˜๊ธฐ diff --git a/ko/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb b/ko/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb index e2261ebb..5f22e58c 100644 --- a/ko/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb +++ b/ko/1_instruction_tuning/notebooks/sft_finetuning_example.ipynb @@ -72,9 +72,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# ๋ฒ ์ด์Šค ๋ชจ๋ธ์„ ํ™œ์šฉํ•œ ๋‹ต๋ณ€ ์ƒ์„ฑ\n", + "# ๊ธฐ๋ณธ ๋ชจ๋ธ์„ ํ™œ์šฉํ•œ ๋‹ต๋ณ€ ์ƒ์„ฑ\n", "\n", - "์—ฌ๊ธฐ์„œ๋Š” ๋Œ€ํ™” ํ…œํ”Œ๋ฆฟ์ด ์—†๋Š” ๋ฒ ์ด์Šค ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•ด๋ณด๊ฒ ์Šต๋‹ˆ๋‹ค." + "์—ฌ๊ธฐ์„œ๋Š” ๋Œ€ํ™” ํ…œํ”Œ๋ฆฟ์ด ์—†๋Š” ๊ธฐ๋ณธ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•ด๋ณด๊ฒ ์Šต๋‹ˆ๋‹ค." ] }, { @@ -83,7 +83,7 @@ "metadata": {}, "outputs": [], "source": [ - "# ํ•™์Šต ์ „ ๋ฒ ์ด์Šค ๋ชจ๋ธ ํ…Œ์ŠคํŠธ ์ง„ํ–‰\n", + "# ํ•™์Šต ์ „ ๊ธฐ๋ณธ ๋ชจ๋ธ ํ…Œ์ŠคํŠธ ์ง„ํ–‰\n", "prompt = \"Write a haiku about programming\"\n", "\n", "# ํ…œํ”Œ๋ฆฟ์œผ๋กœ ๋ฉ”์‹œ์ง€ ํ˜•์‹ ์ง€์ •\n", @@ -143,7 +143,7 @@ "metadata": {}, "outputs": [], "source": [ - "# SFTTrainer ์„ค์ •\n", + "# SFTTrainer๋ฅผ ์œ„ํ•œ SFT configuration ์„ค์ •\n", "sft_config = SFTConfig(\n", " output_dir=\"./sft_output\",\n", " max_steps=1000, # ์›ํ•˜๋Š” ํ•™์Šต ์‹œ๊ฐ„๊ณผ ๋ฐ์ดํ„ฐ์…‹ ํฌ๊ธฐ์— ๋”ฐ๋ผ ์กฐ์ •\n", @@ -220,7 +220,7 @@ "source": [ "# ๋™์ผํ•œ ํ”„๋กฌํ”„ํŠธ๋กœ ๋ฏธ์„ธ ์กฐ์ •๋œ ๋ชจ๋ธ์„ ํ…Œ์ŠคํŠธํ•ด๋ณด์„ธ์š”.\n", "\n", - "# ํ•™์Šต ์ „ ๋ฒ ์ด์Šค ๋ชจ๋ธ ํ…Œ์ŠคํŠธ ์ง„ํ–‰\n", + "# ํ•™์Šต ์ „ ๊ธฐ๋ณธ ๋ชจ๋ธ ํ…Œ์ŠคํŠธ ์ง„ํ–‰\n", "prompt = \"Write a haiku about programming\"\n", "\n", "# ํ…œํ”Œ๋ฆฟ์œผ๋กœ ๋ฉ”์‹œ์ง€ ํ˜•์‹ ์ง€์ •\n", diff --git a/ko/3_parameter_efficient_finetuning/README.md b/ko/3_parameter_efficient_finetuning/README.md new file mode 100644 index 00000000..eca5d888 --- /dev/null +++ b/ko/3_parameter_efficient_finetuning/README.md @@ -0,0 +1,39 @@ +# Parameter-Efficient Fine-Tuning, PEFT + +์–ธ์–ด ๋ชจ๋ธ์ด ์ปค์ง€๋ฉด์„œ ์ „ํ†ต์ ์ธ ๋ฏธ์„ธ ์กฐ์ • ๋ฐฉ์‹์„ ์ ์šฉํ•˜๋Š” ๊ฒƒ์ด ์ ์  ์–ด๋ ค์›Œ์ง€๊ณ  ์žˆ์Šต๋‹ˆ๋‹ค. 1.7B ๋ชจ๋ธ์กฐ์ฐจ๋„ ์ „์ฒด ๋ฏธ์„ธ ์กฐ์ •์„ ์ˆ˜ํ–‰ํ•˜๋ ค๋ฉด ์ƒ๋‹นํ•œ GPU ๋ฉ”๋ชจ๋ฆฌ๊ฐ€ ํ•„์š”ํ•˜๋ฉฐ, ๋ชจ๋ธ ์‚ฌ๋ณธ์„ ๋ณ„๋„๋กœ ์ €์žฅํ•˜๊ธฐ ์œ„ํ•œ ๋น„์šฉ์ด ๋งŽ์ด ๋“ค๊ณ , ๋ชจ๋ธ์˜ ์›๋ž˜ ๋Šฅ๋ ฅ์„ ์ƒ์‹คํ•˜๋Š” ์œ„ํ—˜์ด ์กด์žฌํ•ฉ๋‹ˆ๋‹ค. Parmeter-Efficient Fine-Tuning(PEFT) ๋ฐฉ๋ฒ•์€ ๋Œ€๋ถ€๋ถ„์˜ ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ๊ฐ€ ๊ณ ์ •๋œ ์ƒํƒœ์—์„œ ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ์ผ๋ถ€๋งŒ ์ˆ˜์ •ํ•˜์—ฌ ์ „์ฒด ๋ฏธ์„ธ ์กฐ์ • ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ—™๋‹ˆ๋‹ค. + +ํ•™์Šต ๊ณผ์ •์—์„œ ๋ชจ๋ธ์˜ ๋ชจ๋“  ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์—…๋ฐ์ดํŠธํ•˜๋Š” ์ „ํ†ต์ ์ธ ๋ฏธ์„ธ ์กฐ์ • ๋ฐฉ๋ฒ•์„ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ์— ์ ์šฉํ•˜๋Š” ๊ฒƒ์€ ํ˜„์‹ค์ ์œผ๋กœ ์–ด๋ ต์Šต๋‹ˆ๋‹ค. PEFT๋Š” ์›๋ž˜ ๋ชจ๋ธ ํฌ๊ธฐ์˜ 1% ๋ฏธ๋งŒ์— ํ•ด๋‹นํ•˜๋Š” ํŒŒ๋ผ๋ฏธํ„ฐ๋งŒ ํ•™์Šต์‹œ์ผœ ๋ชจ๋ธ์„ ์กฐ์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•์ž…๋‹ˆ๋‹ค. ํ•™์Šต ๊ฐ€๋Šฅํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ํฌ๊ฒŒ ์ค„์ด๋Š” ๊ฒƒ์€ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์ด์ ์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค: + +- ์ œํ•œ๋œ GPU ๋ฉ”๋ชจ๋ฆฌ๋ฅผ ๊ฐ€์ง„ ํ•˜๋“œ์›จ์–ด์—์„œ๋„ ๋ฏธ์„ธ ์กฐ์ • ๊ฐ€๋Šฅ +- ํšจ์œจ์ ์ธ ํƒœ์Šคํฌ๋ณ„ ์ ์‘ ๋ชจ๋ธ ์ €์žฅ +- ๋ฐ์ดํ„ฐ๊ฐ€ ์ ์€ ์ƒํ™ฉ์—์„œ๋„ ๋›ฐ์–ด๋‚œ ์ผ๋ฐ˜ํ™” ์„ฑ๋Šฅ ์ œ๊ณต +- ๋” ๋น ๋ฅธ ํ•™์Šต ๋ฐ ๋ฐ˜๋ณต ๊ฐ€๋Šฅ + +## ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•œ ๋ฐฉ๋ฒ• + +์ด ๋ชจ๋“ˆ์—์„œ๋Š” ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” ๋‘ ๊ฐ€์ง€ PEFT ๋ฐฉ๋ฒ•์„ ๋‹ค๋ฃน๋‹ˆ๋‹ค: + +### 1๏ธโƒฃ LoRA (Low-Rank Adaptation) + +LoRA๋Š” ํšจ์œจ์ ์ธ ๋ชจ๋ธ ์ ์‘์„ ์œ„ํ•œ ๋ฉ‹์ง„ ์†”๋ฃจ์…˜์„ ์ œ๊ณตํ•˜๋ฉด์„œ ๊ฐ€์žฅ ๋งŽ์ด ์‚ฌ์šฉ๋˜๋Š” PEFT ๋ฐฉ๋ฒ•์œผ๋กœ ์ž๋ฆฌ ์žก์•˜์Šต๋‹ˆ๋‹ค. LoRA๋Š” ์ „์ฒด ๋ชจ๋ธ์„ ์ˆ˜์ •ํ•˜๋Š” ๋Œ€์‹  **ํ•™์Šต ๊ฐ€๋Šฅํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ๋ชจ๋ธ์˜ ์–ดํ…์…˜ ๋ ˆ์ด์–ด์— ์ฃผ์ž…**ํ•ฉ๋‹ˆ๋‹ค. ์ด ์ ‘๊ทผ๋ฒ•์€ ์ „์ฒด ๋ฏธ์„ธ ์กฐ์ •๊ณผ ๋น„์Šทํ•œ ์„ฑ๋Šฅ์„ ์œ ์ง€ํ•˜๋ฉด์„œ ํ•™์Šต ๊ฐ€๋Šฅํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์•ฝ 90%๊นŒ์ง€ ์ค„์ž…๋‹ˆ๋‹ค. [LoRA (Low-Rank Adaptation)](./lora_adapters.md) ์„น์…˜์—์„œ LoRA์— ๋Œ€ํ•ด ์ž์„ธํžˆ ์•Œ์•„๋ณด๊ฒ ์Šต๋‹ˆ๋‹ค. + +### 2๏ธโƒฃ ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹ + +ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹์€ ๋ชจ๋ธ ๊ฐ€์ค‘์น˜๋ฅผ ์ˆ˜์ •ํ•˜๋Š” ๋Œ€์‹  **์ž…๋ ฅ์— ํ•™์Šต ๊ฐ€๋Šฅํ•œ ํ† ํฐ์„ ์ถ”๊ฐ€**ํ•˜์—ฌ **๋” ๊ฒฝ๋Ÿ‰ํ™”๋œ** ์ ‘๊ทผ๋ฒ•์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹์€ LoRA๋งŒํผ ์œ ๋ช…ํ•˜์ง€๋Š” ์•Š์ง€๋งŒ, ๋ชจ๋ธ์„ ์ƒˆ๋กœ์šด ํƒœ์Šคํฌ๋‚˜ ๋„๋ฉ”์ธ์— ๋น ๋ฅด๊ฒŒ ์ ์šฉํ•  ๋•Œ ์œ ์šฉํ•˜๊ฒŒ ์“ฐ์ผ ์ˆ˜ ์žˆ๋Š” ๊ธฐ์ˆ ์ž…๋‹ˆ๋‹ค. [ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹](./prompt_tuning.md) ์„น์…˜์—์„œ ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹์— ๋Œ€ํ•ด ํƒ๊ตฌํ•ด๋ณผ ์˜ˆ์ •์ž…๋‹ˆ๋‹ค. + +## ์‹ค์Šต ๋…ธํŠธ๋ถ + +| ํŒŒ์ผ๋ช… | ์„ค๋ช… | ์‹ค์Šต ๋‚ด์šฉ | ๋งํฌ | Colab | +|-------|-------------|----------|------|-------| +| LoRA Fine-tuning | LoRA ์–ด๋Œ‘ํ„ฐ๋ฅผ ์‚ฌ์šฉํ•ด ๋ชจ๋ธ์„ ๋ฏธ์„ธ ์กฐ์ •ํ•˜๋Š” ๋ฐฉ๋ฒ• ํ•™์Šต | ๐Ÿข LoRA๋ฅผ ์‚ฌ์šฉํ•ด ๋ชจ๋ธ ํ•™์Šตํ•ด๋ณด๊ธฐ
๐Ÿ• ๋‹ค์–‘ํ•œ ๋žญํฌ ๊ฐ’์œผ๋กœ ์‹คํ—˜ํ•ด๋ณด๊ธฐ
๐Ÿฆ ์ „์ฒด ๋ฏธ์„ธ ์กฐ์ •๊ณผ ์„ฑ๋Šฅ ๋น„๊ตํ•ด๋ณด๊ธฐ | [Notebook](./notebooks/finetune_sft_peft.ipynb) | Open In Colab | +| Load LoRA Adapters | LoRA ์–ด๋Œ‘ํ„ฐ๋ฅผ ๋ถˆ๋Ÿฌ์˜ค๊ณ  ํ•™์Šต์‹œํ‚ค๋Š” ๋ฐฉ๋ฒ• ๋ฐฐ์šฐ๊ธฐ | ๐Ÿข ์‚ฌ์ „ ํ•™์Šต๋œ ์–ด๋Œ‘ํ„ฐ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ
๐Ÿ• ๊ธฐ๋ณธ ๋ชจ๋ธ๊ณผ ์–ด๋Œ‘ํ„ฐ ํ•ฉ์น˜๊ธฐ
๐Ÿฆ ์—ฌ๋Ÿฌ ์–ด๋Œ‘ํ„ฐ ๊ฐ„ ์ „ํ™˜ํ•ด๋ณด๊ธฐ | [Notebook](./notebooks/load_lora_adapter_example.ipynb) | Open In Colab | + + +## ์ฐธ๊ณ  +- [Hugging Face PEFT ๋ฌธ์„œ](https://huggingface.co/docs/peft) +- [LoRA ๋…ผ๋ฌธ](https://arxiv.org/abs/2106.09685) +- [QLoRA ๋…ผ๋ฌธ](https://arxiv.org/abs/2305.14314) +- [ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹ ๋…ผ๋ฌธ](https://arxiv.org/abs/2104.08691) +- [Hugging Face PEFT ๊ฐ€์ด๋“œ](https://huggingface.co/blog/peft) +- [How to Fine-Tune LLMs in 2024 with Hugging Face](https://www.philschmid.de/fine-tune-llms-in-2024-with-trl) +- [TRL](https://huggingface.co/docs/trl/index) \ No newline at end of file diff --git a/ko/3_parameter_efficient_finetuning/images/lora_adapter.png b/ko/3_parameter_efficient_finetuning/images/lora_adapter.png new file mode 100644 index 00000000..65aba13f Binary files /dev/null and b/ko/3_parameter_efficient_finetuning/images/lora_adapter.png differ diff --git a/ko/3_parameter_efficient_finetuning/lora_adapters.md b/ko/3_parameter_efficient_finetuning/lora_adapters.md new file mode 100644 index 00000000..7a40baec --- /dev/null +++ b/ko/3_parameter_efficient_finetuning/lora_adapters.md @@ -0,0 +1,124 @@ +# LoRA (Low-Rank Adaptation) + +LoRA๋Š” ๋„๋ฆฌ ์“ฐ์ด๋Š” PEFT ๋ฐฉ๋ฒ•์œผ๋กœ ์ž๋ฆฌ ์žก์•˜์Šต๋‹ˆ๋‹ค. ์–ดํ…์…˜ ๊ฐ€์ค‘์น˜์— ์ž‘์€ ๋žญํฌ ๋ถ„ํ•ด ํ–‰๋ ฌ์„ ์ถ”๊ฐ€ํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ๋™์ž‘์ž‘ํ•˜๋ฉฐ ์ผ๋ฐ˜์ ์œผ๋กœ ํ•™์Šต ๊ฐ€๋Šฅํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์•ฝ 90% ์ค„์—ฌ์ค๋‹ˆ๋‹ค. + +## LoRA ์ดํ•ดํ•˜๊ธฐ + +LoRA(Low-Rank Adaptation)๋Š” ์‚ฌ์ „ ํ•™์Šต๋œ ๋ชจ๋ธ ๊ฐ€์ค‘์น˜๋ฅผ ๊ณ ์ •ํ•œ ์ƒํƒœ์—์„œ ํ•™์Šต ๊ฐ€๋Šฅํ•œ ๋žญํฌ ๋ถ„ํ•ด ํ–‰๋ ฌ์„ ๋ชจ๋ธ ๋ ˆ์ด์–ด์— ์ฃผ์ž…ํ•˜๋Š” ํŒŒ๋ผ๋ฏธํ„ฐ ํšจ์œจ์ ์ธ ๋ฏธ์„ธ ์กฐ์ • ๊ธฐ๋ฒ•์ž…๋‹ˆ๋‹ค. ๋ฏธ์„ธ ์กฐ์ • ๊ณผ์ •์—์„œ ๋ชจ๋“  ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ํ•™์Šต์‹œํ‚ค๋Š” ๋Œ€์‹ , LoRA๋Š” ์ €๋žญํฌ ๋ถ„ํ•ด๋ฅผ ํ†ตํ•ด ๊ฐ€์ค‘์น˜ ์—…๋ฐ์ดํŠธ๋ฅผ ๋” ์ž‘์€ ํ–‰๋ ฌ๋กœ ๋‚˜๋ˆ  ๋ชจ๋ธ ์„ฑ๋Šฅ์€ ์œ ์ง€ํ•˜๋ฉด์„œ ํ•™์Šต ๊ฐ€๋Šฅํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ ์ˆ˜๋ฅผ ํฌ๊ฒŒ ์ค„์ž…๋‹ˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, GPT-3 175B์— LoRA๋ฅผ ์ ์šฉํ–ˆ์„ ๋•Œ ์ „์ฒด ๋ฏธ์„ธ ์กฐ์ • ๋Œ€๋น„ ํ•™์Šต ๊ฐ€๋Šฅํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ ์ˆ˜๋Š” 10,000๋ฐฐ, GPU ๋ฉ”๋ชจ๋ฆฌ ์š”๊ตฌ ์‚ฌํ•ญ์€ 3๋ฐฐ ๊ฐ์†Œํ–ˆ์Šต๋‹ˆ๋‹ค. [LoRA ๋…ผ๋ฌธ](https://arxiv.org/pdf/2106.09685)์—์„œ LoRA์— ๊ด€ํ•œ ์ž์„ธํ•œ ๋‚ด์šฉ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. + +LoRA๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ ํŠธ๋žœ์Šคํฌ๋จธ ๋ ˆ์ด์–ด ์ค‘ ์–ดํ…์…˜ ๊ฐ€์ค‘์น˜์— ๋žญํฌ ๋ถ„ํ•ด ํ–‰๋ ฌ ์Œ์„ ์ถ”๊ฐ€ํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ๋™์ž‘ํ•ฉ๋‹ˆ๋‹ค. ์–ด๋Œ‘ํ„ฐ ๊ฐ€์ค‘์น˜๋Š” ์ถ”๋ก  ๊ณผ์ •์—์„œ ๊ธฐ๋ณธ ๋ชจ๋ธ๊ณผ ๋ณ‘ํ•ฉ๋  ์ˆ˜ ์žˆ๊ณ  ์ถ”๊ฐ€์ ์ธ ์ง€์—ฐ ์‹œ๊ฐ„์ด ๋ฐœ์ƒํ•˜์ง€ ์•Š์Šต๋‹ˆ๋‹ค. LoRA๋Š” ์ž์› ์š”๊ตฌ ์‚ฌํ•ญ์„ ์ ์ ˆํ•œ ์ˆ˜์ค€์œผ๋กœ ์œ ์ง€ํ•˜๋ฉด์„œ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ์„ ํŠน์ • ํƒœ์Šคํฌ๋‚˜ ๋„๋ฉ”์ธ์— ๋งž๊ฒŒ ์กฐ์ •ํ•˜๋Š” ๋ฐ ํŠนํžˆ ์œ ์šฉํ•ฉ๋‹ˆ๋‹ค. + +## LoRA ์–ด๋Œ‘ํ„ฐ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ + +load_adapter()๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์‚ฌ์ „ ํ•™์Šต๋œ ๋ชจ๋ธ์— ์–ด๋Œ‘ํ„ฐ๋ฅผ ๋ถˆ๋Ÿฌ์˜ฌ ์ˆ˜ ์žˆ์œผ๋ฉฐ ๊ฐ€์ค‘์น˜๊ฐ€ ๋ณ‘ํ•ฉ๋˜์ง€ ์•Š์€ ๋‹ค๋ฅธ ์–ด๋Œ‘ํ„ฐ๋ฅผ ์‚ฌ์šฉํ•ด ๋ณผ ๋•Œ ์œ ์šฉํ•ฉ๋‹ˆ๋‹ค. set_adapter() ํ•จ์ˆ˜๋กœ ํ™œ์„ฑ ์–ด๋Œ‘ํ„ฐ ๊ฐ€์ค‘์น˜๋ฅผ ์„ค์ •ํ•ฉ๋‹ˆ๋‹ค. ๊ธฐ๋ณธ ๋ชจ๋ธ์„ ๋ฐ˜ํ™˜ํ•˜๋ ค๋ฉด unload()๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋ถˆ๋Ÿฌ์˜จ ๋ชจ๋“  LoRA ๋ชจ๋“ˆ์„ ๋‚ด๋ฆด ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋ ‡๊ฒŒ ํ•˜๋ฉด ํƒœ์Šคํฌ๋ณ„ ๊ฐ€์ค‘์น˜๋ฅผ ์‰ฝ๊ฒŒ ์ „ํ™˜ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. + +```python +from transformers import AutoModelForCausalLM +from peft import PeftModel + +base_model = AutoModelForCausalLM.from_pretrained("") +peft_model_id = "" +model = PeftModel.from_pretrained(base_model, peft_model_id) +``` + +![lora_load_adapter](./images/lora_adapter.png) + +## LoRA ์–ด๋Œ‘ํ„ฐ ๋ณ‘ํ•ฉ + +LoRA๋กœ ํ•™์Šตํ•œ ํ›„์—๋Š” ๋” ์‰ฌ์šด ๋ฐฐํฌ๋ฅผ ์œ„ํ•ด ์–ด๋Œ‘ํ„ฐ ๊ฐ€์ค‘์น˜๋ฅผ ๊ธฐ๋ณธ ๋ชจ๋ธ์— ๋‹ค์‹œ ๋ณ‘ํ•ฉํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๊ฒฐํ•ฉ๋œ ๊ฐ€์ค‘์น˜๋ฅผ ๊ฐ€์ง„ ๋‹จ์ผ ๋ชจ๋ธ์„ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์ถ”๋ก  ๊ณผ์ •์—์„œ ๋ณ„๋„๋กœ ์–ด๋Œ‘ํ„ฐ๋ฅผ ๋ถˆ๋Ÿฌ์˜ฌ ํ•„์š”๊ฐ€ ์—†์Šต๋‹ˆ๋‹ค. + +๋ณ‘ํ•ฉ ๊ณผ์ •์—์„œ๋Š” ๋ฉ”๋ชจ๋ฆฌ ๊ด€๋ฆฌ์™€ ์ •๋ฐ€๋„์— ์ฃผ์˜ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. ๊ธฐ๋ณธ ๋ชจ๋ธ๊ณผ ์–ด๋Œ‘ํ„ฐ ๊ฐ€์ค‘์น˜๋ฅผ ๋™์‹œ์— ๋ถˆ๋Ÿฌ์™€์•ผ ํ•˜๋ฏ€๋กœ GPU/CPU ๋ฉ”๋ชจ๋ฆฌ๊ฐ€ ์ถฉ๋ถ„ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. `transformers`์˜ `device_map="auto"`๋ฅผ ์‚ฌ์šฉํ•˜๋ฉด ๋ฉ”๋ชจ๋ฆฌ๋ฅผ ์ž๋™์œผ๋กœ ๊ด€๋ฆฌํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ํ•™์Šต ์ค‘ ์‚ฌ์šฉํ•œ ์ •๋ฐ€๋„(์˜ˆ: float16)๋ฅผ ๋ณ‘ํ•ฉ ๊ณผ์ •์—์„œ๋„ ์ผ๊ด€๋˜๊ฒŒ ์œ ์ง€ํ•˜๊ณ , ๋ณ‘ํ•ฉ๋œ ๋ชจ๋ธ์„ ๊ฐ™์€ ํ˜•์‹์œผ๋กœ ์ €์žฅํ•˜์—ฌ ๋ฐฐํฌํ•˜์„ธ์š”. ๋ฐฐํฌ ์ „์— ๋ฐ˜๋“œ์‹œ ๋ณ‘ํ•ฉ๋œ ๋ชจ๋ธ์˜ ์ถœ๋ ฅ ๊ฒฐ๊ณผ์™€ ์„ฑ๋Šฅ ์ง€ํ‘œ๋ฅผ ์–ด๋Œ‘ํ„ฐ ๊ธฐ๋ฐ˜ ๋ฒ„์ „๊ณผ ๋น„๊ตํ•˜์—ฌ ๊ฒ€์ฆํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. + +์–ด๋Œ‘ํ„ฐ๋Š” ์„œ๋กœ ๋‹ค๋ฅธ ํƒœ์Šคํฌ๋‚˜ ๋„๋ฉ”์ธ ๊ฐ„ ์ „ํ™˜๋„ ๊ฐ„ํŽธํ•˜๊ฒŒ ๋งŒ๋“ญ๋‹ˆ๋‹ค. ๊ธฐ๋ณธ ๋ชจ๋ธ๊ณผ ์–ด๋Œ‘ํ„ฐ ๊ฐ€์ค‘์น˜๋ฅผ ๋ณ„๋„๋กœ ๋ถˆ๋Ÿฌ์˜ค๋ฉด ๋น ๋ฅด๊ฒŒ ํƒœ์Šคํฌ๋ณ„ ๊ฐ€์ค‘์น˜๋ฅผ ์ „ํ™˜ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. + +## ๊ตฌํ˜„ ๊ฐ€์ด๋“œ + +`notebooks/` ๋””๋ ‰ํ† ๋ฆฌ์—๋Š” ๋‹ค์–‘ํ•œ PEFT ๋ฐฉ๋ฒ•์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•œ ์‹ค์šฉ์ ์ธ ํŠœํ† ๋ฆฌ์–ผ๊ณผ ์˜ˆ์ œ๊ฐ€ ํฌํ•จ๋˜์–ด ์žˆ์Šต๋‹ˆ๋‹ค. `load_lora_adapter_example.ipynb`์—์„œ ๊ธฐ๋ณธ ์†Œ๊ฐœ๋ฅผ ์‚ดํŽด๋ณธ ๋‹ค์Œ, `lora_finetuning.ipynb`๋ฅผ ํ†ตํ•ด LoRA์™€ SFT๋ฅผ ์‚ฌ์šฉํ•œ ๋ชจ๋ธ ๋ฏธ์„ธ ์กฐ์ • ๊ณผ์ •์„ ๋” ์ž์„ธํžˆ ํƒ๊ตฌํ•ด ๋ณด์„ธ์š”. + +PEFT ๋ฐฉ๋ฒ•์„ ๊ตฌํ˜„ํ•  ๋•Œ๋Š” LoRA์˜ ๋žญํฌ๋ฅผ 4~8 ์ •๋„์˜ ์ž‘์€ ๊ฐ’์œผ๋กœ ์„ค์ •ํ•˜๊ณ  ํ•™์Šต ์†์‹ค์„ ์ง€์†์ ์œผ๋กœ ๋ชจ๋‹ˆํ„ฐ๋งํ•˜๋Š” ๊ฒƒ์ด ์ข‹์Šต๋‹ˆ๋‹ค. ๊ณผ์ ํ•ฉ์„ ๋ฐฉ์ง€ํ•˜๊ธฐ ์œ„ํ•ด ๊ฒ€์ฆ ์„ธํŠธ๋ฅผ ํ™œ์šฉํ•˜๊ณ  ๊ฐ€๋Šฅํ•˜๋‹ค๋ฉด ์ „์ฒด ๋ฏธ์„ธ ์กฐ์ • ๊ธฐ์ค€์„ ๊ณผ ๊ฒฐ๊ณผ๋ฅผ ๋น„๊ตํ•˜์„ธ์š”. ๋‹ค์–‘ํ•œ ํƒœ์Šคํฌ์—์„œ ๊ฐ ๋ฐฉ๋ฒ•์˜ ํšจ๊ณผ๋Š” ๋‹ค๋ฅผ ์ˆ˜ ์žˆ์œผ๋ฏ€๋กœ ์‹คํ—˜์„ ํ†ตํ•ด ์ตœ์ ์˜ ๋ฐฉ๋ฒ•์„ ์ฐพ๋Š” ๊ฒƒ์ด ์ค‘์š”ํ•ฉ๋‹ˆ๋‹ค. + +## OLoRA + +[OLoRA](https://arxiv.org/abs/2406.01775)๋Š” LoRA ์–ด๋Œ‘ํ„ฐ ์ดˆ๊ธฐํ™”๋ฅผ ์œ„ํ•ด QR ๋ถ„ํ•ด๋ฅผ ํ™œ์šฉํ•ฉ๋‹ˆ๋‹ค. OLoRA๋Š” ๋ชจ๋ธ์˜ ๊ธฐ๋ณธ ๊ฐ€์ค‘์น˜๋ฅผ QR ๋ถ„ํ•ด ๊ณ„์ˆ˜์— ๋”ฐ๋ผ ๋ณ€ํ™˜ํ•ฉ๋‹ˆ๋‹ค. ์ฆ‰, ๋ชจ๋ธ ํ•™์Šต ์ „์— ๊ฐ€์ค‘์น˜๋ฅผ ๋ณ€๊ฒฝํ•ฉ๋‹ˆ๋‹ค. ์ด ์ ‘๊ทผ ๋ฐฉ์‹์€ ์•ˆ์ •์„ฑ์„ ํฌ๊ฒŒ ํ–ฅ์ƒ์‹œํ‚ค๊ณ  ์ˆ˜๋ ด ์†๋„๋ฅผ ๋น ๋ฅด๊ฒŒ ํ•˜์—ฌ, ๊ถ๊ทน์ ์œผ๋กœ ๋” ์šฐ์ˆ˜ํ•œ ์„ฑ๋Šฅ์„ ๋‹ฌ์„ฑํ•ฉ๋‹ˆ๋‹ค. + +## PEFT์™€ ํ•จ๊ป˜ TRL ์‚ฌ์šฉํ•˜๊ธฐ + +ํšจ์œจ์ ์ธ ๋ฏธ์„ธ ์กฐ์ •์„ ์œ„ํ•ด PEFT ๋ฐฉ๋ฒ•์„ TRL(Transformers Reinforcements Learning)๊ณผ ๊ฒฐํ•ฉํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ํ†ตํ•ฉ์€ ๋ฉ”๋ชจ๋ฆฌ ์š”๊ตฌ์‚ฌํ•ญ์„ ์ค„์—ฌ์ฃผ๊ธฐ ๋•Œ๋ฌธ์— RLHF (Reinforcement Learning from Human Feedback)์— ํŠนํžˆ ์œ ์šฉํ•ฉ๋‹ˆ๋‹ค. + +```python +from peft import LoraConfig +from transformers import AutoModelForCausalLM + +# PEFT configuration ์„ค์ • +lora_config = LoraConfig( + r=16, + lora_alpha=32, + lora_dropout=0.05, + bias="none", + task_type="CAUSAL_LM" +) + +# ํŠน์ • ๋””๋ฐ”์ด์Šค์—์„œ ๋ชจ๋ธ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ +model = AutoModelForCausalLM.from_pretrained( + "your-model-name", + load_in_8bit=True, # ์„ ํƒ ์‚ฌํ•ญ: 8๋น„ํŠธ ์ •๋ฐ€๋„ ์‚ฌ์šฉ + device_map="auto", + peft_config=lora_config +) +``` + +์œ„ ์ฝ”๋“œ์—์„œ `device_map="auto"`๋ฅผ ์‚ฌ์šฉํ•ด ๋ชจ๋ธ์„ ์ ์ ˆํ•œ ๋””๋ฐ”์ด์Šค์— ์ž๋™์œผ๋กœ ํ• ๋‹นํ–ˆ์Šต๋‹ˆ๋‹ค. `device_map={"": device_index}`๋ฅผ ์จ์„œ ๋ชจ๋ธ์„ ํŠน์ • ๋””๋ฐ”์ด์Šค์— ์ง์ ‘ ํ• ๋‹นํ•  ์ˆ˜๋„ ์žˆ์Šต๋‹ˆ๋‹ค. ๋˜ํ•œ, ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰์„ ํšจ์œจ์ ์œผ๋กœ ์œ ์ง€ํ•˜๋ฉด์„œ ์—ฌ๋Ÿฌ GPU์— ๊ฑธ์ณ ํ•™์Šต์„ ํ™•์žฅํ•  ์ˆ˜๋„ ์žˆ์Šต๋‹ˆ๋‹ค. + +## ๊ธฐ๋ณธ์ ์ธ ๋ณ‘ํ•ฉ ๊ตฌํ˜„ + +LoRA ์–ด๋Œ‘ํ„ฐ ํ•™์Šต์ด ๋๋‚˜๋ฉด ์–ด๋Œ‘ํ„ฐ ๊ฐ€์ค‘์น˜๋ฅผ ๊ธฐ๋ณธ ๋ชจ๋ธ์— ํ•ฉ์น  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ํ•ฉ์น˜๋Š” ๋ฐฉ๋ฒ•์€ ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค: + +```python +import torch +from transformers import AutoModelForCausalLM +from peft import PeftModel + +# 1. ๊ธฐ๋ณธ ๋ชจ๋ธ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ +base_model = AutoModelForCausalLM.from_pretrained( + "base_model_name", + torch_dtype=torch.float16, + device_map="auto" +) + +# 2. ์–ด๋Œ‘ํ„ฐ๊ฐ€ ์žˆ๋Š” PEFT ๋ชจ๋ธ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ +peft_model = PeftModel.from_pretrained( + base_model, + "path/to/adapter", + torch_dtype=torch.float16 +) + +# 3. ์–ด๋Œ‘ํ„ฐ ๊ฐ€์ค‘์น˜๋ฅผ ๊ธฐ๋ณธ ๋ชจ๋ธ์— ๋ณ‘ํ•ฉํ•˜๊ธฐ +try: + merged_model = peft_model.merge_and_unload() +except RuntimeError as e: + print(f"Merging failed: {e}") + # fallback ์ „๋žต ๋˜๋Š” ๋ฉ”๋ชจ๋ฆฌ ์ตœ์ ํ™” ๊ตฌํ˜„ + +# 4. ๋ณ‘ํ•ฉ๋œ ๋ชจ๋ธ ์ €์žฅ +merged_model.save_pretrained("path/to/save/merged_model") +``` + +์ €์žฅ๋œ ๋ชจ๋ธ์˜ ํฌ๊ธฐ๊ฐ€ ์ผ์น˜ํ•˜์ง€ ์•Š์œผ๋ฉด ํ† ํฌ๋‚˜์ด์ €๋„ ํ•จ๊ป˜ ์ €์žฅํ–ˆ๋Š”์ง€ ํ™•์ธํ•˜์„ธ์š”: + +```python +# ๋ชจ๋ธ๊ณผ ํ† ํฌ๋‚˜์ด์ €๋ฅผ ๋ชจ๋‘ ์ €์žฅ +tokenizer = AutoTokenizer.from_pretrained("base_model_name") +merged_model.save_pretrained("path/to/save/merged_model") +tokenizer.save_pretrained("path/to/save/merged_model") +``` + +## ๋‹ค์Œ ๋‹จ๊ณ„ + +โฉ [ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹](prompt_tuning.md) ๊ฐ€์ด๋“œ๋กœ ์ด๋™ํ•ด ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹์œผ๋กœ ๋ฏธ์„ธ ์กฐ์ •ํ•˜๋Š” ๋ฒ•์„ ๋ฐฐ์›Œ๋ณด์„ธ์š”. +โฉ [LoRA ์–ด๋Œ‘ํ„ฐ ํŠœํ† ๋ฆฌ์–ผ](./notebooks/load_lora_adapter.ipynb)์—์„œ LoRA ์–ด๋Œ‘ํ„ฐ๋ฅผ ๋ถˆ๋Ÿฌ์˜ค๋Š” ๋ฐฉ๋ฒ•์„ ๋ฐฐ์›Œ๋ณด์„ธ์š”. + +# ์ฐธ๊ณ  + +- [LORA: LOW-RANK ADAPTATION OF LARGE LANGUAGE MODELS](https://arxiv.org/pdf/2106.09685) +- [Hugging Face PEFT ๋ฌธ์„œ](https://huggingface.co/docs/peft) +- [Hugging Face blog post on PEFT](https://huggingface.co/blog/peft) diff --git a/ko/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb b/ko/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb new file mode 100644 index 00000000..04815633 --- /dev/null +++ b/ko/3_parameter_efficient_finetuning/notebooks/finetune_sft_peft.ipynb @@ -0,0 +1,515 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "z-6LLOPZouLg" + }, + "source": [ + "# Hugging Face TRL๊ณผ LoRA ์–ด๋Œ‘ํ„ฐ๋ฅผ ํ™œ์šฉํ•ด LLM์„ ๋ฏธ์„ธ ์กฐ์ •ํ•˜๋Š” ๋ฐฉ๋ฒ•\n", + "\n", + "์ด ๋…ธํŠธ๋ถ์—์„œ๋Š” LoRA(Low-Rank Adaptation) ์–ด๋Œ‘ํ„ฐ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ์„ ํšจ์œจ์ ์œผ๋กœ ํŒŒ์ธํŠœ๋‹ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์„ค๋ช…ํ•ฉ๋‹ˆ๋‹ค. LoRA๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ํŠน์ง•์„ ๊ฐ€์ง€๋Š” ํŒŒ๋ผ๋ฏธํ„ฐ ํšจ์œจ์ ์ธ ๋ฏธ์„ธ ์กฐ์ • ๊ธฐ๋ฒ•์ž…๋‹ˆ๋‹ค:\n", + "- ์‚ฌ์ „ ํ•™์Šต ๋ชจ๋ธ ๊ฐ€์ค‘์น˜ ๊ณ ์ •\n", + "- ์–ดํ…์…˜ ๋ ˆ์ด์–ด์— ํ•™์Šต ๊ฐ€๋Šฅํ•œ ์ €๋žญํฌ ๋ถ„ํ•ด ํ–‰๋ ฌ ์ถ”๊ฐ€\n", + "- ํ•™์Šต ๊ฐ€๋Šฅํ•œ ํŒŒ๋ผ๋ฏธํ„ฐ ์ˆ˜๊ฐ€ ์ตœ๋Œ€ 90%๊นŒ์ง€ ๊ฐ์†Œ\n", + "- ๋ฉ”๋ชจ๋ฆฌ๋ฅผ ํšจ์œจ์ ์œผ๋กœ ์‚ฌ์šฉํ•˜๋ฉด์„œ ๋ชจ๋ธ ์„ฑ๋Šฅ ์œ ์ง€\n", + "\n", + "์ด ๋…ธํŠธ๋ถ์—์„œ ๋‹ค๋ฃจ๋Š” ๋‚ด์šฉ์ž…๋‹ˆ๋‹ค:\n", + "1. ๊ฐœ๋ฐœ ํ™˜๊ฒฝ ๋ฐ LoRA configuration ์„ค์ •\n", + "2. ์–ด๋Œ‘ํ„ฐ ํ•™์Šต์„ ์œ„ํ•œ ๋ฐ์ดํ„ฐ์…‹ ์ƒ์„ฑ ๋ฐ ์ค€๋น„\n", + "3. `trl`๊ณผ `SFTTrainer`๋ฅผ ํ™œ์šฉํ•œ LoRA ์–ด๋Œ‘ํ„ฐ ๊ธฐ๋ฐ˜ ๋ฏธ์„ธ ์กฐ์ •\n", + "4. ๋ชจ๋ธ ํ…Œ์ŠคํŠธ ๋ฐ ์–ด๋Œ‘ํ„ฐ ๋ณ‘ํ•ฉ(์„ ํƒ ์‚ฌํ•ญ)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fXqd9BXgouLi" + }, + "source": [ + "## 1. ๊ฐœ๋ฐœ ํ™˜๊ฒฝ ์„ค์ •\n", + "\n", + "์ฒซ ๋ฒˆ์งธ ๋‹จ๊ณ„๋Š” trl, transformers, datasets์„ ํฌํ•จํ•œ Hugging Face ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์™€ Pytorch๋ฅผ ์„ค์น˜ํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. trl์„ ๋“ค์–ด๋ณธ ์ ์ด ์—†๋”๋ผ๋„ ๊ฑฑ์ •ํ•˜์ง€ ๋งˆ์„ธ์š”. trl์€ transformers์™€ datasets ์œ„์— ๊ตฌ์ถ•๋œ ์ƒˆ๋กœ์šด ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ๋กœ, ์˜คํ”ˆ ์†Œ์Šค LLM์˜ ๋ฏธ์„ธ ์กฐ์ •๊ณผ RLHF, ์ •๋ ฌ ์ž‘์—…์„ ์‰ฝ๊ฒŒ ๋งŒ๋“ค์–ด์ค๋‹ˆ๋‹ค. \n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tKvGVxImouLi" + }, + "outputs": [], + "source": [ + "# Google Colab์—์„œ requirements ์„ค์น˜\n", + "# !pip install transformers datasets trl huggingface_hub\n", + "\n", + "# Hugging Face ์ธ์ฆ\n", + "\n", + "from huggingface_hub import login\n", + "\n", + "login()\n", + "\n", + "# ํ—ˆ๋ธŒ ํ† ํฐ์„ HF_TOKEN ํ™˜๊ฒฝ ๋ณ€์ˆ˜๋กœ ์„ค์ •ํ•ด๋‘๋ฉด ํŽธํ•˜๊ฒŒ ์‚ฌ์šฉํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XHUzfwpKouLk" + }, + "source": [ + "## 2. ๋ฐ์ดํ„ฐ์…‹ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "z4p6Bvo7ouLk" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DatasetDict({\n", + " train: Dataset({\n", + " features: ['full_topic', 'messages'],\n", + " num_rows: 2260\n", + " })\n", + " test: Dataset({\n", + " features: ['full_topic', 'messages'],\n", + " num_rows: 119\n", + " })\n", + "})" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# ์˜ˆ์‹œ ๋ฐ์ดํ„ฐ์…‹ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ\n", + "from datasets import load_dataset\n", + "\n", + "# TODO: path์™€ name ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์ด์šฉํ•ด ์›ํ•˜๋Š” ๋ฐ์ดํ„ฐ์…‹ ์ •์˜ํ•˜๊ธฐ\n", + "dataset = load_dataset(path=\"HuggingFaceTB/smoltalk\", name=\"everyday-conversations\")\n", + "dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9TOhJdtsouLk" + }, + "source": [ + "## 3. LoRA์™€ ํ•จ๊ป˜ `trl`๊ณผ `SFTTrainer`๋ฅผ ์‚ฌ์šฉํ•œ LLM ๋ฏธ์„ธ ์กฐ์ •\n", + "\n", + "`trl`์—์„œ ์ œ๊ณตํ•˜๋Š” [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer)๋Š” [PEFT](https://huggingface.co/docs/peft/en/index) ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ๋ฅผ ํ†ตํ•ด LoRA ์–ด๋Œ‘ํ„ฐ์™€์˜ ํ†ตํ•ฉ์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. ์ด ์„ค์ •์˜ ์ฃผ์š” ์žฅ์ ์€ ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค:\n", + "\n", + "1. **๋ฉ”๋ชจ๋ฆฌ ํšจ์œจ์„ฑ**: \n", + " - GPU ๋ฉ”๋ชจ๋ฆฌ์— ์–ด๋Œ‘ํ„ฐ ํŒŒ๋ผ๋ฏธํ„ฐ๋งŒ ์ €์žฅ\n", + " - ๊ธฐ๋ณธ ๋ชจ๋ธ ๊ฐ€์ค‘์น˜๋Š” ๊ณ ์ •๋˜๋ฉฐ ๋” ๋‚ฎ์€ ์ •๋ฐ€๋„๋กœ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ ๊ฐ€๋Šฅ\n", + " - ์„œ๋ฒ„๊ฐ€ ์•„๋‹Œ ์†Œ๋น„์ž์šฉ GPU์—์„œ ๋Œ€ํ˜• ๋ชจ๋ธ ๋ฏธ์„ธ ์กฐ์ • ๊ฐ€๋Šฅ\n", + "\n", + "2. **ํ•™์Šต ๊ธฐ๋Šฅ**:\n", + " - ์ตœ์†Œํ•œ์˜ ์„ค์ •์œผ๋กœ ๊ธฐ๋ณธ PEFT/LoRA ํ†ตํ•ฉ\n", + " - ๋” ๋‚˜์€ ๋ฉ”๋ชจ๋ฆฌ ํšจ์œจ์„ ์œ„ํ•œ QLoRA(์–‘์žํ™”๋œ LoRA) ์ง€์›\n", + "\n", + "3. **์–ด๋Œ‘ํ„ฐ ๊ด€๋ฆฌ**:\n", + " - ์ฒดํฌํฌ์ธํŠธ ์ €์žฅ ์‹œ ์–ด๋Œ‘ํ„ฐ ๊ฐ€์ค‘์น˜๋„ ์ €์žฅ\n", + " - ์–ด๋Œ‘ํ„ฐ๋ฅผ ๊ธฐ๋ณธ ๋ชจ๋ธ์— ๋ณ‘ํ•ฉํ•˜๋Š” ๊ธฐ๋Šฅ ์ œ๊ณต\n", + "\n", + "์ด๋ฒˆ ์˜ˆ์ œ์—์„œ๋Š” ์„ฑ๋Šฅ ์ €ํ•˜ ์—†์ด ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰์„ ๋”์šฑ ์ค„์ด๊ธฐ ์œ„ํ•ด 4๋น„ํŠธ ์–‘์žํ™”๋ฅผ ๊ฒฐํ•ฉํ•œ LoRA๋ฅผ ์‚ฌ์šฉํ•˜๊ฒ ์Šต๋‹ˆ๋‹ค. ์„ค์ •์€ ๋ช‡ ๊ฐ€์ง€ ๋‹จ๊ณ„๋งŒ ๊ฑฐ์น˜๋ฉด ๋ฉ๋‹ˆ๋‹ค:\n", + "\n", + "1. LoRA configuration ์„ค์ •(๋žญํฌ, ์•ŒํŒŒ, ๋“œ๋กญ์•„์›ƒ)\n", + "2. PEFT configuration์„ ์ถ”๊ฐ€ํ•œ SFTTrainer ์ƒ์„ฑ\n", + "3. ์–ด๋Œ‘ํ„ฐ ๊ฐ€์ค‘์น˜ ํ•™์Šต ๋ฐ ์ €์žฅ\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ์ฃผ์š” ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "from datasets import load_dataset\n", + "from trl import SFTConfig, SFTTrainer, setup_chat_format\n", + "import torch\n", + "\n", + "device = (\n", + " \"cuda\"\n", + " if torch.cuda.is_available()\n", + " else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\n", + ")\n", + "\n", + "# ๋ชจ๋ธ๊ณผ ํ† ํฌ๋‚˜์ด์ € ๋ถˆ๋Ÿฌ์˜ค๊ธฐ\n", + "model_name = \"HuggingFaceTB/SmolLM2-135M\"\n", + "\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " pretrained_model_name_or_path=model_name\n", + ").to(device)\n", + "tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)\n", + "\n", + "# ๋Œ€ํ™” ํ˜•์‹ ์„ค์ •\n", + "model, tokenizer = setup_chat_format(model=model, tokenizer=tokenizer)\n", + "\n", + "# ๋ฏธ์„ธ ์กฐ์ • ๊ฒฐ๊ณผ๋ฅผ ์ €์žฅํ•˜๊ณ  ์—…๋กœ๋“œํ•˜๊ธฐ ์œ„ํ•œ ์ด๋ฆ„ ์„ค์ •\n", + "finetune_name = \"SmolLM2-FT-MyDataset\"\n", + "finetune_tags = [\"smol-course\", \"module_1\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZbuVArTHouLk" + }, + "source": [ + "`SFTTrainer`๋Š” `peft`์™€์˜ ๊ธฐ๋ณธ ํ†ตํ•ฉ์„ ์ง€์›ํ•˜๊ธฐ ๋•Œ๋ฌธ์— LoRA์™€ ๊ฐ™์€ ๋ฐฉ๋ฒ•์œผ๋กœ LLM ๋ฏธ์„ธ ์กฐ์ • ์ž‘์—…์„ ๋งค์šฐ ์‰ฝ๊ณ  ํšจ์œจ์ ์œผ๋กœ ์ง„ํ–‰ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์šฐ๋ฆฌ๋Š” `LoraConfig`๋ฅผ ์ƒ์„ฑํ•˜๊ณ  ํŠธ๋ ˆ์ด๋„ˆ์—๊ฒŒ ์ œ๊ณตํ•˜๊ธฐ๋งŒ ํ•˜๋ฉด ๋ฉ๋‹ˆ๋‹ค.\n", + "\n", + "
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์—ฐ์Šต: ๋ฏธ์„ธ ์กฐ์ •์„ ์œ„ํ•œ LoRA ํŒŒ๋ผ๋ฏธํ„ฐ ์ •์˜ํ•˜๊ธฐ

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Hugging Face ํ—ˆ๋ธŒ์—์„œ ๊ฐ€์ ธ์˜จ ๋ฐ์ดํ„ฐ์…‹์œผ๋กœ ๋ชจ๋ธ์„ ๋ฏธ์„ธ ์กฐ์ • ํ•ด๋ณด์„ธ์š”.

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๋‚œ์ด๋„

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๐Ÿข ์ผ๋ฐ˜์ ์ธ ํŒŒ๋ผ๋ฏธํ„ฐ ๊ฐ’์„ ์‚ฌ์šฉํ•˜์—ฌ ์ž„์˜๋กœ ๋ฏธ์„ธ ์กฐ์ • ์ง„ํ–‰ํ•˜๊ธฐ

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๐Ÿ• ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์กฐ์ •ํ•˜๊ณ  ๊ฐ€์ค‘์น˜์™€ ํŽธํ–ฅ ๊ฒ€ํ† ํ•ด๋ณด๊ธฐ

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๐Ÿฆ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์กฐ์ •ํ•˜๊ณ  ์ถ”๋ก  ๊ฒฐ๊ณผ ๋ณ€ํ™” ํ™•์ธํ•˜๊ธฐ

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" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "blDSs9swouLk" + }, + "outputs": [], + "source": [ + "from peft import LoraConfig\n", + "\n", + "# TODO: LoRA ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ๊ตฌ์„ฑํ•ด๋ณด์„ธ์š”.\n", + "# r: LoRA ์—…๋ฐ์ดํŠธ ํ–‰๋ ฌ์„ ์œ„ํ•œ ๋žญํฌ ์ฐจ์›์› (์ž‘์„์ˆ˜๋ก ์••์ถ•๋ฅ ์ด ๋†’์•„์ง)\n", + "rank_dimension = 6\n", + "# lora_alpha: LoRA ๋ ˆ์ด์–ด๋ฅผ ์œ„ํ•œ ์Šค์ผ€์ผ๋ง ๊ณ„์ˆ˜ (๋†’์„์ˆ˜๋ก ๋ชจ๋ธ์ด ์ƒˆ๋กœ์šด ๋ฐ์ดํ„ฐ์— ์ž˜ ์ ์‘ํ•จ)\n", + "lora_alpha = 8\n", + "# lora_dropout: LoRA ๋ ˆ์ด์–ด์˜ ๋“œ๋กญ์•„์›ƒ ํ™•๋ฅ  (๊ณผ์ ํ•ฉ ๋ฐฉ์ง€)\n", + "lora_dropout = 0.05\n", + "\n", + "peft_config = LoraConfig(\n", + " r=rank_dimension, # ๋žญํฌ ์ฐจ์› - ์ผ๋ฐ˜์ ์œผ๋กœ 4-32 ์‚ฌ์ด์˜ ๊ฐ’ ์‚ฌ์šฉ\n", + " lora_alpha=lora_alpha, # LoRA ์Šค์ผ€์ผ๋ง ๊ณ„์ˆ˜ - ์ผ๋ฐ˜์ ์œผ๋กœ ๋žญํฌ ์ฐจ์›์˜ 2๋ฐฐ\n", + " lora_dropout=lora_dropout, # LoRA ๋ ˆ์ด์–ด์˜์˜ ๋“œ๋กญ์•„์›ƒ ํ™•๋ฅ \n", + " bias=\"none\", # LoRA ํŽธํ–ฅ ์œ ํ˜• - ํ•ด๋‹น ํŽธํ–ฅ์€ ํ•™์Šต ์ค‘ ์—…๋ฐ์ดํŠธ๋จ๋จ\n", + " target_modules=\"all-linear\", # LoRA๋ฅผ ์ ์šฉํ•  ๋ชจ๋“ˆ๋“ˆ\n", + " task_type=\"CAUSAL_LM\", # ๋ชจ๋ธ ๊ตฌ์กฐ์— ๋งž๋Š” ํƒœ์Šคํฌ ์œ ํ˜•\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "l5NUDPcaouLl" + }, + "source": [ + "ํ•™์Šต์„ ์‹œ์ž‘ํ•˜๊ธฐ ์ „ ์‚ฌ์šฉํ•˜๊ณ ์ž ํ•˜๋Š” ํ•˜์ดํผํŒŒ๋ผ๋ฏธํ„ฐ(`TrainingArguments`) ๊ฐ’์„ ์„ค์ •ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NqT28VZlouLl" + }, + "outputs": [], + "source": [ + "# ํ•™์Šต์„ ์œ„ํ•œ configuration\n", + "# QLoRA ๋…ผ๋ฌธ์— ๊ธฐ๋ฐ˜ํ•œ ํ•˜์ดํผํŒŒ๋ผ๋ฏธํ„ฐ ์„ค์ •\n", + "args = SFTConfig(\n", + " # ์ถœ๋ ฅ ์„ค์ •\n", + " output_dir=finetune_name, # ๋ชจ๋ธ ์ฒดํฌํฌ์ธํŠธ ์ €์žฅ ๋””๋ ‰ํ† ๋ฆฌ\n", + " # ํ•™์Šต ๊ธฐ๊ฐ„\n", + " num_train_epochs=1, # ํ•™์Šต ์—ํฌํฌ ์ˆ˜\n", + " # ๋ฐฐ์น˜ ํฌ๊ธฐ ์„ค์ •\n", + " per_device_train_batch_size=2, # GPU๋‹น ๋ฐฐ์น˜ ํฌ๊ธฐ\n", + " gradient_accumulation_steps=2, # ๋” ํฐ ๋ฐฐ์น˜ ํฌ๊ธฐ ํšจ๊ณผ๋ฅผ ์œ„ํ•œ ๊ฒฝ์‚ฌ ๋ˆ„์ \n", + " # ๋ฉ”๋ชจ๋ฆฌ ์ตœ์ ํ™”\n", + " gradient_checkpointing=True, # ํ•™์Šต ์†๋„๋Š” ๋Š๋ ค์ง€์ง€๋งŒ ๋ฉ”๋ชจ๋ฆฌ ์ ˆ์•ฝ ๊ฐ€๋Šฅ\n", + " # ์˜ตํ‹ฐ๋งˆ์ด์ € ์„ค์ •\n", + " optim=\"adamw_torch_fused\", # ํšจ์œจ์„ฑ์„ ์œ„ํ•ด fused AdamW ์‚ฌ์šฉ\n", + " learning_rate=2e-4, # ํ•™์Šต๋ฅ  (QLoRA ๋…ผ๋ฌธ ์ฐธ๊ณ )\n", + " max_grad_norm=0.3, # ๊ฒฝ์‚ฌ ํด๋ฆฌํ•‘ ์ž„๊ณ—๊ฐ’\n", + " # ํ•™์Šต๋ฅ  ์Šค์ผ€์ค„๋ง\n", + " warmup_ratio=0.03, # ํ•™์Šต๋ฅ ์„ 0์—์„œ ๋ชฉํ‘œ ๊ฐ’๊นŒ์ง€ ์„ ํ˜•์ ์œผ๋กœ ์ฆ๊ฐ€์‹œํ‚ค๋Š” ์›œ์—…์„ ์œ„ํ•œ ์ „์ฒด ํ•™์Šต ์Šคํ…์˜ ๋น„์œจ\n", + " lr_scheduler_type=\"constant\", # ์›œ์—… ํ›„ ์ผ์ •ํ•œ ํ•™์Šต๋ฅ  ์œ ์ง€\n", + " # ๋กœ๊น… ๋ฐ ์ €์žฅ\n", + " logging_steps=10, # ๋งค N ์Šคํ…๋งˆ๋‹ค ์ง€ํ‘œ ๊ธฐ๋ก\n", + " save_strategy=\"epoch\", # ๋งค ์—ํฌํฌ๋งˆ๋‹ค ์ฒดํฌํฌ์ธํŠธ ์ €์žฅ\n", + " # ์ •๋ฐ€๋„ ์„ค์ •\n", + " bf16=True, # bfloat16 ์ •๋ฐ€๋„ ์‚ฌ์šฉ\n", + " # ํ†ตํ•ฉ ์„ค์ •\n", + " push_to_hub=False, # Hugging Face ํ—ˆ๋ธŒ๋กœ ๋‚ด๋ณด๋‚ด์ง€ ์•Š์Œ\n", + " report_to=None, # ์™ธ๋ถ€ ๋กœ๊น… ๋น„ํ™œ์„ฑํ™”\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cGhR7uFBouLl" + }, + "source": [ + "์ด์ œ ๋ชจ๋ธ ํ•™์Šต์„ ์œ„ํ•œ `SFTTrainer` ์ƒ์„ฑ์— ํ•„์š”ํ•œ ์š”์†Œ๋ฅผ ๋ชจ๋‘ ๊ฐ–์ท„์Šต๋‹ˆ๋‹ค." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "M00Har2douLl" + }, + "outputs": [], + "source": [ + "max_seq_length = 1512 # ๋ชจ๋ธ๊ณผ ๋ฐ์ดํ„ฐ์…‹ ํŒจํ‚น์„ ์œ„ํ•œ ์ตœ๋Œ€ ์‹œํ€€์Šค ๊ธธ์ด\n", + "\n", + "# LoRA configuration์œผ๋กœ SFTTrainer ์ƒ์„ฑ\n", + "trainer = SFTTrainer(\n", + " model=model,\n", + " args=args,\n", + " train_dataset=dataset[\"train\"],\n", + " peft_config=peft_config, # LoRA configuration\n", + " max_seq_length=max_seq_length, # ์ตœ๋Œ€ ์‹œํ€€์Šค ๊ธธ์ด\n", + " tokenizer=tokenizer,\n", + " packing=True, # ํšจ์œจ์„ฑ์„ ์œ„ํ•ด ์ž…๋ ฅ ํŒจํ‚น ํ™œ์„ฑํ™”\n", + " dataset_kwargs={\n", + " \"add_special_tokens\": False, # ํ…œํ”Œ๋ฆฟ์—์„œ ์ถ”๊ฐ€ ํ† ํฐ ์ฒ˜๋ฆฌ\n", + " \"append_concat_token\": False, # ์ถ”๊ฐ€ ๊ตฌ๋ถ„์ž ์—†์Œ \n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zQ_kRN24ouLl" + }, + "source": [ + "`Trainer` ๊ฐ์ฒด์˜ `train()` ๋ฉ”์„œ๋“œ๋ฅผ ํ˜ธ์ถœํ•ด ํ•™์Šต์„ ์‹œ์ž‘ํ•ฉ๋‹ˆ๋‹ค. ํ•™์Šต ๋ฃจํ”„๊ฐ€ ์‹œ์ž‘๋˜๊ณ  3 ์—ํฌํฌ ๋™์•ˆ ๋ชจ๋ธ์ด ํ•™์Šต๋ฉ๋‹ˆ๋‹ค. ์ง€๊ธˆ์€ PEFT ๋ฐฉ๋ฒ•์„ ์“ฐ๊ณ  ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์ „์ฒด ๋ชจ๋ธ์„ ์ €์žฅํ•˜์ง€ ์•Š๊ณ  ์ ์‘๋œ ๋ชจ๋ธ ๊ฐ€์ค‘์น˜๋งŒ ์ €์žฅํ•ฉ๋‹ˆ๋‹ค." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Tq4nIYqKouLl" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "300e5dfbb4b54750b77324345c7591f9", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/72 [00:00\n", + "

์ถ”๊ฐ€ ์—ฐ์Šต: LoRA ์–ด๋Œ‘ํ„ฐ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ

\n", + "

์˜ˆ์ œ ๋…ธํŠธ๋ถ์—์„œ ๋ฐฐ์šด ๋‚ด์šฉ์„ ํ™œ์šฉํ•˜์—ฌ ํ•™์Šต๋œ LoRA ์–ด๋Œ‘ํ„ฐ๋ฅผ ๋ถˆ๋Ÿฌ์˜ค๊ณ  ์ถ”๋ก ์— ์‚ฌ์šฉํ•˜์„ธ์š”.

\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "id": "I5B494OdouLl" + }, + "outputs": [], + "source": [ + "# ๋ฉ”๋ชจ๋ฆฌ ํ™•๋ณด\n", + "del model\n", + "del trainer\n", + "torch.cuda.empty_cache()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "P1UhohVdouLl" + }, + "outputs": [], + "source": [ + "import torch\n", + "from peft import AutoPeftModelForCausalLM\n", + "from transformers import AutoTokenizer, pipeline\n", + "\n", + "# PEFT ์–ด๋Œ‘ํ„ฐ๊ฐ€ ์žˆ๋Š” ๋ชจ๋ธ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ\n", + "tokenizer = AutoTokenizer.from_pretrained(finetune_name)\n", + "model = AutoPeftModelForCausalLM.from_pretrained(\n", + " finetune_name, device_map=\"auto\", torch_dtype=torch.float16\n", + ")\n", + "pipe = pipeline(\n", + " \"text-generation\", model=merged_model, tokenizer=tokenizer, device=device\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "99uFDAuuouLl" + }, + "source": [ + "๋ช‡ ๊ฐ€์ง€ ํ”„๋กฌํ”„ํŠธ ์˜ˆ์ œ๋กœ ํ…Œ์ŠคํŠธํ•ด๋ณด๊ณ  ๋ชจ๋ธ์ด ์–ด๋–ป๊ฒŒ ์ž‘๋™ํ•˜๋Š”์ง€ ํ™•์ธํ•ด๋ด…์‹œ๋‹ค." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "id": "-shSmUbvouLl", + "outputId": "16d97c61-3b31-4040-c780-3c4de75c3824" + }, + "outputs": [], + "source": [ + "prompts = [\n", + " \"What is the capital of Germany? Explain why thats the case and if it was different in the past?\",\n", + " \"Write a Python function to calculate the factorial of a number.\",\n", + " \"A rectangular garden has a length of 25 feet and a width of 15 feet. If you want to build a fence around the entire garden, how many feet of fencing will you need?\",\n", + " \"What is the difference between a fruit and a vegetable? Give examples of each.\",\n", + "]\n", + "\n", + "\n", + "def test_inference(prompt):\n", + " prompt = pipe.tokenizer.apply_chat_template(\n", + " [{\"role\": \"user\", \"content\": prompt}],\n", + " tokenize=False,\n", + " add_generation_prompt=True,\n", + " )\n", + " outputs = pipe(\n", + " prompt,\n", + " )\n", + " return outputs[0][\"generated_text\"][len(prompt) :].strip()\n", + "\n", + "\n", + "for prompt in prompts:\n", + " print(f\" prompt:\\n{prompt}\")\n", + " print(f\" response:\\n{test_inference(prompt)}\")\n", + " print(\"-\" * 50)" + ] + } + ], + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/ko/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb b/ko/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb new file mode 100644 index 00000000..d9cb26da --- /dev/null +++ b/ko/3_parameter_efficient_finetuning/notebooks/load_lora_adapter.ipynb @@ -0,0 +1,5061 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "otj46qRbtpnd", + "outputId": "0fa81296-44b7-479d-e03b-7ef196cc89e4" + }, + "outputs": [], + "source": [ + "!pip install -q bitsandbytes datasets accelerate loralib\n", + "!pip install -q git+https://github.com/huggingface/transformers.git@main git+https://github.com/huggingface/peft.git" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 446, + "referenced_widgets": [ + 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"4f1b47e2d7d64765b4f12dcc16878f1f", + "993fcaf75d47402d9f9f7c2b094295f1", + "e6787d84d8234d368dc48f8d43bdb2a1", + "5ecb188fa26a499ea67954aa90108f6e", + "1d3184ff92b7465da9f36eeb5726f021", + "74af354462ec4d4a83654c5b8ba7a2f9" + ] + }, + "id": "cg3fiQOvmI3Q", + "outputId": "92fcd520-ad2c-4488-8f66-664ffc566b89" + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0\"\n", + "import torch\n", + "import torch.nn as nn\n", + "import bitsandbytes as bnb\n", + "from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "S65GcxNGA9kz" + }, + "source": [ + "## Hub์—์„œ ์–ด๋Œ‘ํ„ฐ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 517, + "referenced_widgets": [ + "ff2454cf69b346fea70070522cf93689", + "87b36eb4ad3b4047a32c7d67a5aabc5e", + 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get_peft_model\n", + "from peft import PeftModel, PeftConfig\n", + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "\n", + "peft_model_id = \"ybelkada/opt-6.7b-lora\"\n", + "config = PeftConfig.from_pretrained(peft_model_id)\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " config.base_model_name_or_path,\n", + " return_dict=True,\n", + " load_in_8bit=True,\n", + " device_map=\"auto\",\n", + ")\n", + "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n", + "\n", + "# LoRA ๋ชจ๋ธ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ\n", + "model = PeftModel.from_pretrained(model, peft_model_id)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MDqJWba-tpnv", + "outputId": "dd65644f-6921-46e1-920c-400bd7d888cf" + }, + "outputs": [], + "source": [ + "batch = tokenizer(\"Machine learning is: \", return_tensors='pt')\n", + "\n", + "with torch.cuda.amp.autocast():\n", + " output_tokens = model.generate(**batch, max_new_tokens=50)\n", + "\n", + "print('\\n\\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "machine_shape": "hm", + "provenance": [] + }, + "gpuClass": "standard", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.4" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "003dc6b3dc1f4bffa7335aa5d35b1e74": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + 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์ถ”๊ฐ€ํ•˜๊ณ  ์ตœ์ ํ™”ํ•ฉ๋‹ˆ๋‹ค. + +## ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹ ์ดํ•ดํ•˜๊ธฐ + +ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹์€ ๋ชจ๋ธ ๋ฏธ์„ธ ์กฐ์ •์˜ ๋Œ€์•ˆ์œผ๋กœ, ํ•™์Šต ๊ฐ€๋Šฅํ•œ ์—ฐ์† ๋ฒกํ„ฐ(์†Œํ”„ํŠธ ํ”„๋กฌํ”„ํŠธ)๋ฅผ ์ž…๋ ฅ ํ…์ŠคํŠธ์— ์ถ”๊ฐ€ํ•˜๋Š” ํŒŒ๋ผ๋ฏธํ„ฐ ํšจ์œจ์ ์ธ ๋ฐฉ์‹์ž…๋‹ˆ๋‹ค. ์†Œํ”„ํŠธ ํ”„๋กฌํ”„ํŠธ๋Š” ์ด์‚ฐ์ ์ธ ํ…์ŠคํŠธ ํ”„๋กฌํ”„ํŠธ์™€ ๋‹ค๋ฅด๊ฒŒ ๋ชจ๋ธ ํŒŒ๋ผ๋ฏธํ„ฐ๊ฐ€ ๊ณ ์ •๋œ ์ƒํƒœ์—์„œ ์—ญ์ „ํŒŒ๋ฅผ ํ†ตํ•ด ํ•™์Šต๋ฉ๋‹ˆ๋‹ค. ์ด ๋ฐฉ์‹์€ ["The Power of Scale for Parameter-Efficient Prompt Tuning"](https://arxiv.org/abs/2104.08691) (Lester et al., 2021)์—์„œ ์ฒ˜์Œ ์†Œ๊ฐœ๋˜์—ˆ์œผ๋ฉฐ, ์ด ๋…ผ๋ฌธ์€ ๋ชจ๋ธ ํฌ๊ธฐ๊ฐ€ ์ปค์งˆ์ˆ˜๋ก ๋ฏธ์„ธ ์กฐ์ •๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹์ด ๊ฒฝ์Ÿ๋ ฅ์„ ๊ฐ€์ง„๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์˜€์Šต๋‹ˆ๋‹ค. ๋…ผ๋ฌธ์— ๋”ฐ๋ฅด๋ฉด, ์•ฝ 100์–ต ๊ฐœ์˜ ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ๊ฐ€์ง„ ๋ชจ๋ธ์—์„œ ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹์˜ ์„ฑ๋Šฅ์€ ํƒœ์Šคํฌ๋‹น ์ˆ˜๋ฐฑ ๊ฐœ์˜ ๋งค๊ฐœ๋ณ€์ˆ˜ ์ˆ˜์ •๋งŒ์œผ๋กœ ๋ชจ๋ธ ๋ฏธ์„ธ ์กฐ์ •์˜ ์„ฑ๋Šฅ๊ณผ ์ผ์น˜ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ์Šต๋‹ˆ๋‹ค. + +๋ชจ๋ธ์˜ ์ž„๋ฒ ๋”ฉ ๊ณต๊ฐ„์— ์กด์žฌํ•˜๋Š” ์—ฐ์†์ ์ธ ๋ฒกํ„ฐ์ธ ์†Œํ”„ํŠธ ํ”„๋กฌํ”„ํŠธ๋Š” ํ•™์Šต ๊ณผ์ •์—์„œ ์ตœ์ ํ™”๋ฉ๋‹ˆ๋‹ค. ์ž์—ฐ์–ด ํ† ํฐ์„ ์‚ฌ์šฉํ•˜๋Š” ์ „ํ†ต์ ์ธ ์ด์‚ฐ ํ”„๋กฌํ”„ํŠธ์™€ ๋‹ฌ๋ฆฌ ์†Œํ”„ํŠธ ํ”„๋กฌํ”„ํŠธ๋Š” ๊ณ ์œ ํ•œ ์˜๋ฏธ๋ฅผ ๊ฐ€์ง€๊ณ  ์žˆ์ง€๋Š” ์•Š์ง€๋งŒ ๊ฒฝ์‚ฌ ํ•˜๊ฐ•๋ฒ•์„ ํ†ตํ•ด ํŒŒ๋ผ๋ฏธํ„ฐ๊ฐ€ ๊ณ ์ •๋œ ๋ชจ๋ธ์—์„œ ์›ํ•˜๋Š” ๋™์ž‘์„ ์ด๋Œ์–ด๋‚ด๋Š” ๋ฐฉ๋ฒ•์„ ํ•™์Šตํ•ฉ๋‹ˆ๋‹ค. ์ด ๊ธฐ๋ฒ•์€ ๋ชจ๋ธ ์ „์ฒด๋ฅผ ๋ณต์‚ฌํ•˜์ง€ ์•Š๊ณ  ์ ์€ ์ˆ˜(์ผ๋ฐ˜์ ์œผ๋กœ ๋ช‡๋ฐฑ ๊ฐœ์˜ ํŒŒ๋ผ๋ฏธํ„ฐ)์— ํ•ด๋‹นํ•˜๋Š” ํ”„๋กฌํ”„ํŠธ ๋ฒกํ„ฐ๋งŒ์„ ์ €์žฅํ•˜๊ธฐ ๋•Œ๋ฌธ์— ๋ฉ€ํ‹ฐ ํƒœ์Šคํฌ ์‹œ๋‚˜๋ฆฌ์˜ค์— ๋”์šฑ ํšจ๊ณผ์ ์ž…๋‹ˆ๋‹ค. ์ด ์ ‘๊ทผ ๋ฐฉ์‹์€ ์ตœ์†Œํ•œ์˜ ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰์„ ์œ ์ง€ํ•  ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ํ”„๋กฌํ”„ํŠธ ๋ฒกํ„ฐ๋งŒ ๊ต์ฒดํ•˜์—ฌ ๋ชจ๋ธ์„ ๋‹ค์‹œ ๋กœ๋“œํ•˜์ง€ ์•Š๊ณ ๋„ ์‹ ์†ํ•˜๊ฒŒ ์ž‘์—…์„ ์ „ํ™˜ํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•ฉ๋‹ˆ๋‹ค. + +## ํ•™์Šต ๊ณผ์ • + +์†Œํ”„ํŠธ ํ”„๋กฌํ”„ํŠธ๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ 8~32๊ฐœ์˜ ํ† ํฐ์œผ๋กœ ๊ตฌ์„ฑ๋˜๋ฉฐ ๋ฌด์ž‘์œ„ ๋˜๋Š” ๊ธฐ์กด ํ…์ŠคํŠธ๋กœ ์ดˆ๊ธฐํ™”ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ดˆ๊ธฐํ™” ๋ฐฉ๋ฒ•์€ ํ•™์Šต ๊ณผ์ •์—์„œ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜๋ฉฐ ํ…์ŠคํŠธ ๊ธฐ๋ฐ˜ ์ดˆ๊ธฐํ™”๊ฐ€ ๋ฌด์ž‘์œ„ ์ดˆ๊ธฐํ™”๋ณด๋‹ค ์ข‹์€ ์„ฑ๋Šฅ์„ ๋ณด์ด๋Š” ๊ฒฝ์šฐ๊ฐ€ ๋งŽ์Šต๋‹ˆ๋‹ค. + +ํ•™์Šต์ด ์ง„ํ–‰๋˜๋Š” ๋™์•ˆ ๊ธฐ๋ณธ ๋ชจ๋ธ์˜ ํŒŒ๋ผ๋ฏธํ„ฐ๋Š” ๊ณ ์ •๋˜์–ด ์žˆ๊ณ  ํ”„๋กฌํ”„ํŠธ ํŒŒ๋ผ๋ฏธํ„ฐ๋งŒ ์—…๋ฐ์ดํŠธ ๋ฉ๋‹ˆ๋‹ค. ํ”„๋กฌํ”„ํŠธ ํŒŒ๋ผ๋ฏธํ„ฐ์— ์ดˆ์ ์„ ๋งž์ถ˜ ์ด ์ ‘๊ทผ ๋ฐฉ์‹์€ ์ผ๋ฐ˜์ ์ธ ํ•™์Šต ๋ชฉํ‘œ๋ฅผ ๋”ฐ๋ฅด์ง€๋งŒ ํ•™์Šต๋ฅ ๊ณผ ํ”„๋กฌํ”„ํŠธ ํ† ํฐ์˜ ๊ฒฝ์‚ฌ ๋™์ž‘์— ์„ธ์‹ฌํ•œ ์ฃผ์˜๊ฐ€ ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค. + +## PEFT ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ๋ฅผ ํ™œ์šฉํ•œ ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹ ๊ตฌํ˜„ + +PEFT ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ๋ฅผ ์ด์šฉํ•ด ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹์„ ์ง๊ด€์ ์œผ๋กœ ๊ตฌํ˜„ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ๊ธฐ๋ณธ์ ์ธ ์˜ˆ์ œ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์Šต๋‹ˆ๋‹ค: + +```python +from peft import PromptTuningConfig, TaskType, get_peft_model +from transformers import AutoModelForCausalLM, AutoTokenizer + +# ๊ธฐ๋ณธ ๋ชจ๋ธ ๋ถˆ๋Ÿฌ์˜ค๊ธฐ +model = AutoModelForCausalLM.from_pretrained("your-base-model") +tokenizer = AutoTokenizer.from_pretrained("your-base-model") + +# ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹ configuration ์„ค์ • +peft_config = PromptTuningConfig( + task_type=TaskType.CAUSAL_LM, + num_virtual_tokens=8, # ํ•™์Šต ๊ฐ€๋Šฅํ•œ ํ† ํฐ ์ˆ˜ + prompt_tuning_init="TEXT", # ํ…์ŠคํŠธ ๊ธฐ๋ฐ˜ ์ดˆ๊ธฐํ™” + prompt_tuning_init_text="Classify if this text is positive or negative:", + tokenizer_name_or_path="your-base-model", +) + +# ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹ ๊ฐ€๋Šฅํ•œ ๋ชจ๋ธ ์ƒ์„ฑ +model = get_peft_model(model, peft_config) +``` + +## ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹๊ณผ ๋‹ค๋ฅธ PEFT ๋ฐฉ๋ฒ• ๋น„๊ต + +๋‹ค๋ฅธ PEFT ์ ‘๊ทผ ๋ฐฉ์‹๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹์€ ํšจ์œจ์„ฑ์ด ๋›ฐ์–ด๋‚ฉ๋‹ˆ๋‹ค. LoRA๋Š” ํŒŒ๋ผ๋ฏธํ„ฐ ์ˆ˜์™€ ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰์ด ์ ์ง€๋งŒ ํƒœ์Šคํฌ ์ „ํ™˜์„ ์œ„ํ•ด ์–ด๋Œ‘ํ„ฐ๋ฅผ ๋ถˆ๋Ÿฌ์™€์•ผ ํ•˜๋Š” ๋ฐ˜๋ฉด ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹์€ ์ž์›์„ ๋” ์ ๊ฒŒ ์‚ฌ์šฉํ•˜๋ฉด์„œ ์ฆ‰๊ฐ์ ์ธ ํƒœ์Šคํฌ ์ „ํ™˜์ด ๊ฐ€๋Šฅํ•ฉ๋‹ˆ๋‹ค. ๋ฐ˜๋ฉด์— ์ „์ฒด ๋ฏธ์„ธ ์กฐ์ •์€ ์ƒ๋‹นํ•œ ์–‘์˜ ์ž์›์„ ํ•„์š”๋กœ ํ•˜๊ณ  ์„œ๋กœ ๋‹ค๋ฅธ ํƒœ์Šคํฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด ํƒœ์Šคํฌ์— ๋งž๋Š” ๊ฐ๊ฐ์˜ ๋ชจ๋ธ์„ ํ•„์š”๋กœ ํ•ฉ๋‹ˆ๋‹ค. + +| ๋ฐฉ๋ฒ• | ํŒŒ๋ผ๋ฏธํ„ฐ ์ˆ˜ | ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰ | ํƒœ์Šคํฌ ์ „ํ™˜ | +|--------|------------|---------|----------------| +| ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹ | ๋งค์šฐ ์ ์Œ | ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰ ์ตœ์†Œํ™” | ์‰ฌ์›€ | +| LoRA | ์ ์Œ | ์ ์Œ | ๋ชจ๋ธ์„ ๋‹ค์‹œ ๋ถˆ๋Ÿฌ์™€์•ผ ํ•จ | +| ์ „์ฒด ๋ฏธ์„ธ ์กฐ์ • | ๋งŽ์Œ | ๋งŽ์Œ | ์ƒˆ๋กœ์šด ๋ชจ๋ธ ๋ณต์‚ฌ ํ•„์š” | + +ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹์„ ๊ตฌํ˜„ํ•  ๋•Œ๋Š” ์ ์€ ์ˆ˜(8~16๊ฐœ)์˜ ๊ฐ€์ƒ ํ† ํฐ์œผ๋กœ ์‹œ์ž‘ํ•œ ํ›„ ํƒœ์Šคํฌ์˜ ๋ณต์žก์„ฑ์— ๋”ฐ๋ผ ํ† ํฐ ์ˆ˜๋ฅผ ์ ์ฐจ ๋Š˜๋ ค๊ฐ€์„ธ์š”. ํƒœ์Šคํฌ์™€ ๊ด€๋ จ๋œ ํ…์ŠคํŠธ๋กœ ์ดˆ๊ธฐํ™”๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ๊ฒƒ์ด ๋ฌด์ž‘์œ„ ์ดˆ๊ธฐํ™”๋ณด๋‹ค ์ผ๋ฐ˜์ ์œผ๋กœ ๋” ๋‚˜์€ ๊ฒฐ๊ณผ๋ฅผ ๊ฐ€์ ธ์˜ต๋‹ˆ๋‹ค. ์ดˆ๊ธฐํ™” ์ „๋žต์€ ๋ชฉํ‘œ ํƒœ์Šคํฌ์˜ ๋ณต์žก์„ฑ์„ ๋ฐ˜์˜ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. + +ํ•™์Šต ๊ณผ์ •์€ ์ „์ฒด ๋ฏธ์„ธ ์กฐ์ •๊ณผ ์•ฝ๊ฐ„์˜ ์ฐจ์ด๊ฐ€ ์žˆ์Šต๋‹ˆ๋‹ค. ๋ณดํ†ต ๋†’์€ ํ•™์Šต๋ฅ ์ด ํšจ๊ณผ์ ์ด์ง€๋งŒ ํ”„๋กฌํ”„ํŠธ ํ† ํฐ ๊ฒฝ์‚ฌ๋ฅผ ์‹ ์ค‘ํ•˜๊ฒŒ ๋ชจ๋‹ˆํ„ฐ๋งํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. ๋‹ค์–‘ํ•œ ์˜ˆ์ œ๋ฅผ ํ™œ์šฉํ•œ ์ฃผ๊ธฐ์ ์ธ ๊ฒ€์ฆ์€ ์„œ๋กœ ๋‹ค๋ฅธ ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ ๊ฐ•๋ ฅํ•œ ์„ฑ๋Šฅ์„ ๋ณด์žฅํ•˜๋Š” ๋ฐ ๋„์›€์ด ๋ฉ๋‹ˆ๋‹ค. + +## ์‘์šฉ + +ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹์€ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์‹œ๋‚˜๋ฆฌ์˜ค์—์„œ ํŠนํžˆ ๋›ฐ์–ด๋‚ฉ๋‹ˆ๋‹ค: + +1. ๋ฉ€ํ‹ฐ ํƒœ์Šคํฌ ๋ฐฐํฌ +2. ์ž์›์ด ์ œํ•œ์ ์ธ ํ™˜๊ฒฝ +3. ์‹ ์†ํ•œ ํƒœ์Šคํฌ ์ ์‘ +4. ํ”„๋ผ์ด๋ฒ„์‹œ์— ๋ฏผ๊ฐํ•œ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜ + +๊ทธ๋Ÿฌ๋‚˜ ๋ชจ๋ธ ํฌ๊ธฐ๊ฐ€ ์ž‘์•„์งˆ์ˆ˜๋ก ์ „์ฒด ๋ฏธ์„ธ ์กฐ์ •๊ณผ ๋น„๊ตํ–ˆ์„ ๋•Œ ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹์˜ ๊ฒฝ์Ÿ๋ ฅ์ด ๋–จ์–ด์ง‘๋‹ˆ๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, SmolLM2 ํฌ๊ธฐ์˜ ๋ชจ๋ธ์€ ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹๋ณด๋‹ค ์ „์ฒด ๋ฏธ์„ธ ์กฐ์ •๋ฅผ ์ˆ˜ํ–‰ํ•˜๋Š” ๊ฒƒ์ด ์ข€ ๋” ์˜๋ฏธ์žˆ์Šต๋‹ˆ๋‹ค. + +## ๋‹ค์Œ ๋‹จ๊ณ„ + +โญ๏ธ [LoRA ์–ด๋Œ‘ํ„ฐ ํŠœํ† ๋ฆฌ์–ผ](./notebooks/finetune_sft_peft.ipynb)์—์„œ LoRA ์–ด๋Œ‘ํ„ฐ๋ฅผ ํ™œ์šฉํ•ด ๋ชจ๋ธ์„ ๋ฏธ์„ธ ์กฐ์ •ํ•˜๋Š” ๋ฒ•์„ ๋ฐฐ์›Œ๋ด…์‹œ๋‹ค. + +## ์ฐธ๊ณ  +- [Hugging Face PEFT ๋ฌธ์„œ](https://huggingface.co/docs/peft) +- [ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹ ๋…ผ๋ฌธ](https://arxiv.org/abs/2104.08691) +- [Hugging Face Cookbook](https://huggingface.co/learn/cookbook/prompt_tuning_peft) diff --git a/ko/README.md b/ko/README.md new file mode 100644 index 00000000..8d6abe0f --- /dev/null +++ b/ko/README.md @@ -0,0 +1,93 @@ +![smolcourse image](../banner.png) + +# ์†Œํ˜• ์–ธ์–ด ๋ชจ๋ธ ๊ณผ์ • + +์ด ๊ณผ์ •์—์„œ๋Š” ํŠน์ • ์‚ฌ์šฉ ์‚ฌ๋ก€์— ๋งž๊ฒŒ ์–ธ์–ด ๋ชจ๋ธ์„ ์ •๋ ฌํ•˜๋Š” ๋ฒ•์„ ๋‹ค๋ฃน๋‹ˆ๋‹ค. ๋ชจ๋“  ์ž๋ฃŒ๋Š” ๋Œ€๋ถ€๋ถ„์˜ ๋กœ์ปฌ ์ปดํ“จํ„ฐ์—์„œ ์‹คํ–‰๋˜๋ฏ€๋กœ ๊ฐ„ํŽธํ•˜๊ฒŒ ์–ธ์–ด ๋ชจ๋ธ ์ •๋ ฌ์„ ์‹œ์ž‘ํ•ด๋ณผ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ๊ณผ์ •์„ ์œ„ํ•ด ํ•„์š”ํ•œ ์ตœ์†Œํ•œ์˜ GPU ์š”๊ตฌ ์‚ฌํ•ญ์ด๋‚˜ ์œ ๋ฃŒ ์„œ๋น„์Šค๊ฐ€ ์—†์Šต๋‹ˆ๋‹ค. [SmolLM2](https://github.com/huggingface/smollm/tree/main) ์‹œ๋ฆฌ์ฆˆ ๋ชจ๋ธ์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ๊ณผ์ •์ด์ง€๋งŒ, ์—ฌ๊ธฐ์„œ ๋ฐฐ์šด ๊ธฐ์ˆ ์„ ๋” ํฐ ๋ชจ๋ธ์ด๋‚˜ ๋‹ค๋ฅธ ์ž‘์€ ์–ธ์–ด ๋ชจ๋ธ๋กœ ์˜ฎ๊ธธ ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. + + + + + +
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์ง€๊ธˆ ๋ฐ”๋กœ ์ฐธ์—ฌํ•˜์„ธ์š”!

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์ด ๊ณผ์ •์€ ์—ด๋ ค ์žˆ์œผ๋ฉฐ ๋‹ค๋ฅธ ์‚ฌ์šฉ์ž์™€์˜ ์ƒํ˜ธ ๊ฒ€ํ† ๋ฅผ ์ง„ํ–‰ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด ๊ณผ์ •์— ์ฐธ์—ฌํ•˜๋ ค๋ฉด pull request(PR)๋ฅผ ์—ด๊ณ  ๊ฒ€ํ†  ๋ฐ›์„ ์ˆ˜ ์žˆ๋„๋ก ๊ฒฐ๊ณผ๋ฌผ์„ ์ œ์ถœํ•˜์„ธ์š”. ๋‹ค์Œ ๋‹จ๊ณ„๋ฅผ ๋”ฐ๋ฅด๋ฉด ๋ฉ๋‹ˆ๋‹ค:

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  1. ์—ฌ๊ธฐ์—์„œ ๋ ˆํฌ์ง€ํ† ๋ฆฌ๋ฅผ forkํ•˜์„ธ์š”.
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  2. ์ž๋ฃŒ๋ฅผ ์ฝ๊ณ , ๋ฐ”๊ฟ” ๋ณด๊ณ , ์‹ค์Šตํ•ด๋ณด๊ณ , ๋‚˜๋งŒ์˜ ์˜ˆ์ œ๋ฅผ ์ถ”๊ฐ€ํ•ด๋ณด์„ธ์š”.
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  3. december-2024 ๋ธŒ๋žœ์น˜์— PR์„ ๋ณด๋‚ด์„ธ์š”.
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  4. ๊ฒ€ํ† ๊ฐ€ ๋๋‚˜๋ฉด december-2024 ๋ธŒ๋žœ์น˜์— ๋ณ‘ํ•ฉ๋ฉ๋‹ˆ๋‹ค.
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์ด ๊ณผ์ •์€ ํ•™์Šต์— ๋„์›€์ด ๋  ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ง€์†์ ์œผ๋กœ ๋ฐœ์ „ํ•˜๋Š” ์ปค๋ฎค๋‹ˆํ‹ฐ ๊ธฐ๋ฐ˜ ์ฝ”์Šค๋ฅผ ํ˜•์„ฑํ•˜๋Š” ๋ฐ์—๋„ ๊ธฐ์—ฌํ•  ๊ฒƒ์ž…๋‹ˆ๋‹ค.

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+ +[discussion thread](https://github.com/huggingface/smol-course/discussions/2#discussion-7602932)์—์„œ ๊ณผ์ •์— ๋Œ€ํ•ด ํ† ๋ก ํ•  ์ˆ˜๋„ ์žˆ์Šต๋‹ˆ๋‹ค. + +## ๊ณผ์ • ๊ฐœ์š” + +์ด ๊ณผ์ •์€ ์†Œํ˜• ์–ธ์–ด ๋ชจ๋ธ์˜ ์ดˆ๊ธฐ ํ•™์Šต๋ถ€ํ„ฐ ๊ฒฐ๊ณผ๋ฌผ ๋ฐฐํฌ๊นŒ์ง€ ์‹ค์Šตํ•  ์ˆ˜ ์žˆ๋Š” ์‹ค์šฉ์ ์ธ ๋‚ด์šฉ์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค. + +| ๋ชจ๋“ˆ | ์„ค๋ช… | ์ƒํƒœ | ๊ณต๊ฐœ์ผ | +|--------|-------------|---------|--------------| +| [Instruction Tuning](./1_instruction_tuning) | ์ง€๋„ ํ•™์Šต ๊ธฐ๋ฐ˜ ๋ฏธ์„ธ ์กฐ์ •, ๋Œ€ํ™” ํ…œํ”Œ๋ฆฟ ์ž‘์„ฑ, ๊ธฐ๋ณธ์ ์ธ ์ง€์‹œ๋ฅผ ๋”ฐ๋ฅด๊ฒŒ ํ•˜๋Š” ๋ฐฉ๋ฒ• ํ•™์Šต | โœ… ํ•™์Šต ๊ฐ€๋Šฅ | 2024. 12. 3 | +| [Preference Alignment](./2_preference_alignment) | ๋ชจ๋ธ์„ ์ธ๊ฐ„ ์„ ํ˜ธ๋„์— ๋งž๊ฒŒ ์ •๋ ฌํ•˜๊ธฐ ์œ„ํ•œ DPO์™€ ORPO ๊ธฐ๋ฒ• ํ•™์Šต | โœ… ํ•™์Šต ๊ฐ€๋Šฅ | 2024. 12. 6 | +| [Parameter-efficient Fine-tuning](./3_parameter_efficient_finetuning) | LoRA, ํ”„๋กฌํ”„ํŠธ ํŠœ๋‹์„ ํฌํ•จํ•œ ํšจ์œจ์ ์ธ ์ ์‘ ๋ฐฉ๋ฒ• ํ•™์Šต | โœ… ํ•™์Šต ๊ฐ€๋Šฅ | 2024. 12. 9 | +| [Evaluation](./4_evaluation) | ์ž๋™ ๋ฒค์น˜๋งˆํฌ ์‚ฌ์šฉ๋ฒ• ๋ฐ ์‚ฌ์šฉ์ž ์ •์˜ ๋„๋ฉ”์ธ ํ‰๊ฐ€ ์ˆ˜ํ–‰ ๋ฐฉ๋ฒ• ํ•™์Šต | โœ… ํ•™์Šต ๊ฐ€๋Šฅ | 2024. 12. 13 | +| [Vision-language Models](./5_vision_language_models) | ๋น„์ „-์–ธ์–ด ํƒœ์Šคํฌ๋ฅผ ์œ„ํ•œ ๋ฉ€ํ‹ฐ๋ชจ๋‹ฌ ๋ชจ๋ธ ์ ์šฉ ๋ฐฉ๋ฒ• ํ•™์Šต | [๐Ÿšง ์ค€๋น„์ค‘](https://github.com/huggingface/smol-course/issues/49) | 2024. 12. 16 | +| [Synthetic Datasets](./6_synthetic_datasets) | ๋ชจ๋ธ ํ•™์Šต์„ ์œ„ํ•œ ํ•ฉ์„ฑ ๋ฐ์ดํ„ฐ์…‹ ์ƒ์„ฑ ๋ฐ ๊ฒ€์ฆ | [๐Ÿšง ์ค€๋น„์ค‘](https://github.com/huggingface/smol-course/issues/83) | 2024. 12. 20 | +| [Inference](./7_inference) | ๋ชจ๋ธ์˜ ํšจ์œจ์ ์ธ ์ถ”๋ก  ๋ฐฉ๋ฒ• ํ•™์Šต | ๐Ÿ“ ์ž‘์„ฑ ์˜ˆ์ • | 2024. 12. 23 | + +## ์™œ ์†Œํ˜• ์–ธ์–ด ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜๋‚˜์š”? + +๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ์€ ๋›ฐ์–ด๋‚œ ๋Šฅ๋ ฅ์„ ๋ณด์—ฌ์ฃผ์ง€๋งŒ, ์ƒ๋‹นํ•œ ์—ฐ์‚ฐ ์ž์›์„ ํ•„์š”๋กœ ํ•˜๋ฉฐ ํŠน์ • ๊ธฐ๋Šฅ์— ์ดˆ์ ์„ ๋งž์ถ˜ ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์— ๋Œ€ํ•ด์„œ๋Š” ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ์ด ๊ณผํ•œ ๊ฒฝ์šฐ๋„ ์žˆ์Šต๋‹ˆ๋‹ค. ์†Œํ˜• ์–ธ์–ด ๋ชจ๋ธ์€ ๋„๋ฉ”์ธ ํŠนํ™” ์• ํ”Œ๋ฆฌ์ผ€์ด์…˜์— ์žˆ์–ด์„œ ๋ช‡ ๊ฐ€์ง€ ์ด์ ์„ ์ œ๊ณตํ•ฉ๋‹ˆ๋‹ค: + +- **ํšจ์œจ์„ฑ**: ๋Œ€ํ˜• ์–ธ์–ด ๋ชจ๋ธ๋ณด๋‹ค ํ›จ์”ฌ ์ ์€ ์—ฐ์‚ฐ ์ž์›์œผ๋กœ ํ•™์Šต ๋ฐ ๋ฐฐํฌ ๊ฐ€๋Šฅ +- **๋งž์ถคํ™”**: ํŠน์ • ๋„๋ฉ”์ธ์— ๋Œ€ํ•œ ๋ฏธ์„ธ ์กฐ์ • ๋ฐ ์ ์‘ ์šฉ์ด +- **์ œ์–ด**: ๋ชจ๋ธ ๋™์ž‘ ๊ณผ์ •์„ ์ž˜ ์ดํ•ดํ•  ์ˆ˜ ์žˆ๊ณ  ๋ชจ๋ธ์˜ ๋™์ž‘์„ ์‰ฝ๊ฒŒ ์ œ์–ด ๊ฐ€๋Šฅ +- **๋น„์šฉ**: ํ•™์Šต๊ณผ ์ถ”๋ก  ๊ณผ์ •์—์„œ ํ•„์š”ํ•œ ๋น„์šฉ ๊ฐ์†Œ +- **ํ”„๋ผ์ด๋ฒ„์‹œ**: ๋ฐ์ดํ„ฐ๋ฅผ ์™ธ๋ถ€ API๋กœ ๋ณด๋‚ด์ง€ ์•Š๊ณ  ๋กœ์ปฌ์—์„œ ์‹คํ–‰ ๊ฐ€๋Šฅ +- **์นœํ™˜๊ฒฝ**: ํƒ„์†Œ ๋ฐœ์ž๊ตญ์„ ์ค„์ด๋Š” ํšจ์œจ์ ์ธ ์ž์› ์‚ฌ์šฉ +- **์‰ฌ์šด ํ•™์ˆ  ์—ฐ๊ตฌ ๋ฐ ๊ฐœ๋ฐœ**: ์ตœ์‹  LLM์„ ํ™œ์šฉํ•ด ๋ฌผ๋ฆฌ์  ์ œ์•ฝ ์—†์ด ํ•™์ˆ  ์—ฐ๊ตฌ๋ฅผ ์‰ฝ๊ฒŒ ์‹œ์ž‘ํ•  ์ˆ˜ ์žˆ๋„๋ก ์ง€์› + +## ์‚ฌ์ „ ์ค€๋น„ ์‚ฌํ•ญ + +์‹œ์ž‘ํ•˜๊ธฐ ์ „์— ์•„๋ž˜ ์‚ฌํ•ญ์ด ์ค€๋น„๋˜์–ด ์žˆ๋Š”์ง€ ํ™•์ธํ•˜์„ธ์š”: +- ๋จธ์‹ ๋Ÿฌ๋‹๊ณผ ์ž์—ฐ์–ด์ฒ˜๋ฆฌ์— ๋Œ€ํ•œ ๊ธฐ๋ณธ์ ์ธ ์ดํ•ด๊ฐ€ ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค. +- Python, PyTorch ๋ฐ `transformers` ๋ผ์ด๋ธŒ๋Ÿฌ๋ฆฌ์— ์ต์ˆ™ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. +- ์‚ฌ์ „ ํ•™์Šต๋œ ์–ธ์–ด ๋ชจ๋ธ๊ณผ ๋ ˆ์ด๋ธ”์ด ์žˆ๋Š” ์•ก์„ธ์Šคํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. + +## ์„ค์น˜ + +์ด ๊ณผ์ •์€ ํŒจํ‚ค์ง€ ํ˜•ํƒœ๋กœ ๊ด€๋ฆฌ๋˜๊ธฐ ๋•Œ๋ฌธ์— ํŒจํ‚ค์ง€ ๋งค๋‹ˆ์ €๋ฅผ ์ด์šฉํ•ด ์˜์กด์„ฑ ์„ค์น˜๋ฅผ ์‰ฝ๊ฒŒ ์ง„ํ–‰ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด [uv](https://github.com/astral-sh/uv) ์‚ฌ์šฉ์„ ๊ถŒ์žฅํ•˜์ง€๋งŒ `pip`๋‚˜ `pdm`์„ ์‚ฌ์šฉํ•  ์ˆ˜๋„ ์žˆ์Šต๋‹ˆ๋‹ค. + +### `uv` ์‚ฌ์šฉ + +`uv`๋ฅผ ์„ค์น˜ํ•œ ํ›„, ์•„๋ž˜ ๋ช…๋ น์–ด๋กœ ์†Œํ˜• ์–ธ์–ด ๋ชจ๋ธ ๊ณผ์ •์„ ์„ค์น˜ํ•  ์ˆ˜ ์žˆ์Šต๋‹ˆ๋‹ค: + +```bash +uv venv --python 3.11.0 +uv sync +``` + +### `pip` ์‚ฌ์šฉ + +๋ชจ๋“  ์˜ˆ์ œ๋Š” ๋™์ผํ•œ **python 3.11** ํ™˜๊ฒฝ์—์„œ ์‹คํ–‰๋˜๊ธฐ ๋•Œ๋ฌธ์— ์•„๋ž˜์ฒ˜๋Ÿผ ํ™˜๊ฒฝ์„ ์ƒ์„ฑํ•˜๊ณ  ์˜์กด์„ฑ์„ ์„ค์น˜ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค: + +```bash +# python -m venv .venv +# source .venv/bin/activate +pip install -r requirements.txt +``` + +### Google Colab + +**Google Colab**์—์„œ๋Š” ์‚ฌ์šฉํ•˜๋Š” ํ•˜๋“œ์›จ์–ด์— ๋”ฐ๋ผ ์œ ์—ฐํ•˜๊ฒŒ ์˜์กด์„ฑ์„ ์„ค์น˜ํ•ด์•ผ ํ•ฉ๋‹ˆ๋‹ค. ์•„๋ž˜์™€ ๊ฐ™์ด ์ง„ํ–‰ํ•˜์„ธ์š”: + +```bash +pip install transformers trl datasets huggingface_hub +``` + +## ์ฐธ์—ฌ + +๋งŽ์€ ์‚ฌ๋žŒ์ด ๊ณ ๊ฐ€์˜ ์žฅ๋น„ ์—†์ด LLM์„ ๋ฏธ์„ธ ์กฐ์ •ํ•˜๋Š” ๋ฒ•์„ ๋ฐฐ์šธ ์ˆ˜ ์žˆ๋„๋ก ์ด ์ž๋ฃŒ๋ฅผ ๊ณต์œ ํ•ด ๋ด…์‹œ๋‹ค! + +[![Star History Chart](https://api.star-history.com/svg?repos=huggingface/smol-course&type=Date)](https://star-history.com/#huggingface/smol-course&Date) diff --git a/pull_request_template.md b/pull_request_template.md index f01b592d..7c51ff78 100644 --- a/pull_request_template.md +++ b/pull_request_template.md @@ -27,7 +27,7 @@ List any notebooks you've added or modified: - [ ] I have read the module materials - [ ] My code runs without errors - [ ] I have pushed models and datasets to the huggingface hub -- [ ] My PR is based on the `december_2024` branch +- [ ] My PR is based on the `december-2024` branch ## Questions or Discussion Points Add any questions you have or points you'd like to discuss: