Multimodal Large Language Models (MLLMs) have attracted much attention for their multifunctionality. However, traditional Transformer architectures incur significant overhead due to their secondary computational complexity. To address this issue, we introduce ML-Mamba, a multimodal language model, which utilizes the latest and efficient Mamba-2 model for inference. Mamba-2 is known for its linear scalability and fast processing of long sequences. We replace the Transformer-based backbone with a pre-trained Mamba-2 model and explore methods for integrating 2D visual selective scanning mechanisms into multimodal learning while also trying various visual encoders and Mamba-2 model variants. Our extensive experiments in various multimodal benchmark tests demonstrate the competitive performance of ML-Mamba and highlight the potential of state space models in multimodal tasks. The experimental results show that: (1) We empirically explore the application of 2D visual selective scanning in multimodal learning and propose the Mamba-2 Scan Connector (MSC) to enhance representational capabilities. (2) ML-Mamba achieves performance comparable to state-of-the-art methods such as TinyLaVA and MobileVLM v2 through its linear sequential modeling while faster inference speed; (3) Compared to multimodal models utilizing Mamba-1, the Mamba-2-based ML-Mamba exhibits superior inference performance and effectiveness.
The code will be released gradually in the next few days.
(2024/08/12) The evaluation code has been uploaded!
(2024/08/12) Our model weight is available now.
(2024/08/12) The training and inference codes are released.
(2024/07/25) The repository is created.
This repository was built using Python 3.10, but should be backwards compatible with any Python >= 3.8. We require PyTorch 2.1 or greater installation instructions can be found here. This repository was developed and has been thoroughly tested with PyTorch 2.1.0 and Torchvision 0.16.0.
Once PyTorch has been properly installed, you can install this package locally via an editable installation (or via
pip install git+https://github.com/WenjunHuang94/ML-Mamba):
conda create -n ML-Mamba python=3.10
conda activate ML-Mamba
git clone https://github.com/WenjunHuang94/ML-Mamba
cd ML-Mamba
pip install -e .
# install mamba and other packages
cd causal-conv1d-main && pip install -e .
cd .. && git clone https://github.com/WenjunHuang94/mamba
cd mamba && pip install -e .
cd .. && pip install packaging ninja
# Verify Ninja --> should return exit code "0"
ninja --version; echo $?
# option
pip install --upgrade Pillow
pip install --upgrade numpy
pip install --upgrade huggingface_hub
pip install numpy==1.21.2
pip install --upgrade clickIf you run into any problems during the installation process, please file a GitHub Issue.
Once installed, loading and running inference with pretrained ML-Mamba models is easy:
First, you need to create a .hf_token file in the ML-Mamba project directory and fill in your Huggingface token.
import requests
import torch
from PIL import Image
from pathlib import Path
from mlmamba import load
hf_token = Path(".hf_token").read_text().strip()
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# In case your GPU does not support bf16
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
# Load a pretrained VLM (either local path, or ID to auto-download from the HF Hub)
model_id = "mlmamba+3b"
vlm = load(model_id, hf_token=hf_token)
vlm.to(device, dtype=dtype)
image = Image.open("pic/test0.png").convert("RGB")
user_prompt = "Provide a detailed description of this image"
# Build prompt
prompt_builder = vlm.get_prompt_builder()
prompt_builder.add_turn(role="human", message=user_prompt)
prompt_text = prompt_builder.get_prompt()
# Generate!
generated_text = vlm.generate(
image,
prompt_text,
use_cache=True,
do_sample=True,
temperature=1.0,
max_new_tokens=512,
)
print(f'user_prompt : {user_prompt} \ngenerated_text : {generated_text}')For a complete terminal-based CLI for interacting with our VLMs, check out scripts/generate.py.
Our pretrained-models weight is available now.
The LLaVa v1.5 Instruct Dataset can be downloaded by the automated download script in scripts/preprocess.py:
# Download the `llava-v1.5-instruct` (Instruct Tuning) Image and Language Data (includes extra post-processing)
python scripts/preprocess.py --dataset_id "llava-v1.5-instruct" --root_dir <PATH-TO-DATA-ROOT>
# (In case you also wish to download the explicit vision-language alignment data)
python scripts/preprocess.py --dataset_id "llava-laion-cc-sbu-558k" --root_dir <PATH-TO-DATA-ROOT>LVIS-Instruct-4V and LRV-Instruct can also be downloaded by the scripts in scripts/additional-datasets.
Here's how you would train ML-Mamba follow the training recipe in our paper across 8 GPUs on a single-node:
First, you need to create a .hf_token file in the ML-Mamba/scripts project directory and fill in your Huggingface token.
# Run from the root of the repository
torchrun --standalone --nnodes 1 --nproc-per-node 8 scripts/pretrain.py \
--model.vision_backbone_id "dinosiglip-vit-so-384px" \
--model.image_resize_strategy "resize-naive" \
--model.llm_backbone_id "mamba2-2.7b" \
--model.type "mlmamba+3b" \
--model.finetune_global_batch_size 128 \
--model.finetune_per_device_batch_size 8 \
--dataset.type "llava-v15"This project is released under the MIT License
@misc{huang2024mlmamba,
title={ML-Mamba: Efficient Multi-Modal Large Language Model Utilizing Mamba-2},
author={Wenjun Huang and Jianguo Hu},
year={2024},
eprint={2407.19832},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2407.19832},
}
This repository is built based on LLaVA, Mamba, Transformers, and Cobra for their public code release.