这个文档提供了了转换LLaMA同结构模型的方法。
LLaMA类模型有着基本相同的结构，但权重和prompt构造有差异。在fastllm中，通过转转模型时修改部分配置，实现对这些变体模型的支持、
以下配置方案根据模型的源代码整理，不保证模型推理结果与原版完全一致。
目前，转换脚本和两行加速方式均可用于llama类模型。但无论采用哪一种方式，都需要预留足够的内存（可以用swap空间）。
在float16模式下，转换时约需要4×参数量+1GB的空闲内存。
这里以支持推理各类Llama结构的基座模型为例，介绍如何应用本文档。
- 方案一：修改转换脚本
以alpaca2flm.py为模板修改。在创建model之后添加：
model = LlamaForCausalLM.from_pretrained(model_name).float()
# config.json中定义了自己的model_type的需要添加
conf = model.config.__dict__
conf["model_type"] = "llama"
# 接下来的部分各个Chat模型有差别，Base模型有的需要添加pre_prompt。
torch2flm.tofile(exportPath, model, tokenizer, pre_prompt = "",
user_role = "", bot_role = "", history_sep = "",
dtype = dtype)其中，pre_prompt 、user_role 、bot_role 、history_sep分别为“开始的系统提示词（第一轮对话之前）”，“用户角色标志”，“用户话语结束标志及模型回复开始标志”，“两轮对话之间的分隔符”。
- 方案二：修改config.json
在下载的模型目录下，修改配置文件
config.json中，修改"model_type"为llama，并增加下面的键-值对：
"pre_prompt": "",
"user_role": "",
"bot_role": "",
"history_sep": "",如需添加Token ID而非字符串（类似baichuan-chat模型），可以使用“<FLM_FIX_TOKEN_{ID}>”的格式添加。
- 执行脚本
python3 tools/alpaca2flm.py [输出文件名] [精度] [原始模型名称或路径] conf = model.config.__dict__
conf["model_type"] = "llama"
llm.from_hf(model, tokenizer, pre_prompt = "",
user_role = "", bot_role = "", history_sep = "",
dtype = dtype)如果想使fastllm模型和原版transformers模型基本一致，最主要的操作是对齐tokenizer。
如果模型使用了huggingface 加速版本的Tokenizers（即模型目录中包含tokenizer.json并优先使用），目前的转换脚本仅在从本地文件转换时，能够对齐tokenizer。
注意检查原始tokenizer的encode()方法返回的结果前面是否会加空格。如果原始tokenizer没有加空格，则需要设置：
conf["tokenizer_add_dummy_prefix"] = False见上方“修改方案”。
一部分模型需要制定bos_token_id，假设bos_token_id为1则可以配置如下：
torch2flm.tofile(exportPath, model, tokenizer, pre_prompt = "<FLM_FIX_TOKEN_1>",
user_role = "", bot_role = "", history_sep = "",
dtype = dtype)对Chat Model，同样是修改转换脚本，或修改模型的config.json，以下是目前常见的chat model的配置：
- internlm/internlm-chat-7b
- internlm/internlm-chat-7b v1.1
- internlm/internlm-chat-20b
conf = model.config.__dict__
conf["model_type"] = "llama"
torch2flm.tofile(exportPath, model, tokenizer, pre_prompt = "<s><s>",
user_role = "<|User|>:", bot_role = "<eoh>\n<|Bot|>:",
history_sep = "<eoa>\n<s>", dtype = dtype)可以直接使用llamalike2flm.py脚本转换：
cd build
python3 tools/llamalike2flm.py internlm-7b-fp16.flm float16 internlm/internlm-chat-20b #导出float16模型
python3 tools/llamalike2flm.py internlm-7b-int8.flm int8 internlm/internlm-chat-20b #导出int8模型
python3 tools/llamalike2flm.py internlm-7b-int4.flm int4 internlm/internlm-chat-20b #导出int4模型
python3 tools/llamalike2flm.py internlm-7b-int4.flm float16 internlm/internlm-chat-7b #导出internlm-chat-7b float16模型- internlm/internlm2-chat-1_8b
- internlm/internlm2-chat-7b
- internlm/internlm2-chat-20b
使用llamalike2flm.py脚本转换：
cd build
python3 tools/llamalike2flm.py internlm2-1.8b-fp16.flm float16 iinternlm/internlm2-chat-1_8b #导出1.8B float16模型
python3 tools/llamalike2flm.py internlm2-7b-fp16.flm float16 internlm/internlm2-chat-7b #导出chat-7b float16模型
python3 tools/llamalike2flm.py internlm2-7b-int8.flm int8 internlm/internlm2-chat-7b #导出chat-7b int8模型- xverse/XVERSE-13B-Chat
- xverse/XVERSE-7B-Chat
conf = model.config.__dict__
conf["model_type"] = "llama"
conf["tokenizer_add_dummy_prefix"] = False
torch2flm.tofile(exportPath, model, tokenizer, pre_prompt = "",
user_role = "Human: ", bot_role = "\n\nAssistant: ",
history_sep = "<FLM_FIX_TOKEN_3>", dtype = dtype)XVERSE-13B-Chat V1 版本需要对输入做NFKC规范化，fastllm暂不支持，因此需要使用原始tokenizer.
- xverse/XVERSE-13B-256K
该模型没有将RoPE外推参数放到config中，因此需要手工指定：
conf = model.config.__dict__
conf["model_type"] = "llama"
conf["rope_theta"] = 500000
conf["rope_scaling.type"] = "dynamic"
conf["rope_scaling.factor"] = 2.0
conf["tokenizer_add_dummy_prefix"] = False
torch2flm.tofile(exportPath, model, tokenizer, pre_prompt = "",
user_role = "Human: ", bot_role = "\n\nAssistant: ",
history_sep = "<FLM_FIX_TOKEN_3>", dtype = dtype)- Vicuna v1.1 v1.3
torch2flm.tofile(exportPath, model, tokenizer,
pre_prompt="A chat between a curious user and an artificial intelligence assistant. "
"The assistant gives helpful, detailed, and polite answers to the user's questions. "
user_role="USER: ", bot_role=" ASSISTANT:", history_sep="<s>", dtype=dtype)- BiLLa
torch2flm.tofile(exportPath, model, tokenizer, pre_prompt = "\n",
user_role = "Human: ", bot_role = "\nAssistant: ",
history_sep = "\n", dtype = dtype)- meta-llama/Llama-2-chat
| Model | Llama2-chat | Llama2-chat-hf |
|---|---|---|
| 7B | meta-llama/Llama-2-7b-chat | meta-llama/Llama-2-7b-chat-hf |
| 13B | meta-llama/Llama-2-13b-chat | meta-llama/Llama-2-13b-chat-hf |
| Model | CodeLlama-Instruct |
|---|---|
| 7B | codellama/CodeLlama-7b-Instruct-hf |
| 13B | codellama/CodeLlama-13b-Instruct-hf |
官方示例代码中，可以不用系统提示语：
torch2flm.tofile(exportPath, model, tokenizer, pre_prompt = "<FLM_FIX_TOKEN_1>",
user_role = "[INST] ", bot_role = " [/INST]",
history_sep = " <FLM_FIX_TOKEN_2><FLM_FIX_TOKEN_1>", dtype = dtype)Llama-2系列支持系统提示语需要修改代码，单轮可以使用以下带有系统提示语的版本：
torch2flm.tofile(exportPath, model, tokenizer,
pre_prompt = "<FLM_FIX_TOKEN_1>[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, " \
"while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. " \
"Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, " \
"or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, " \
"please don't share false information.\n<</SYS>>\n\n",
user_role = " ", bot_role = " [/INST]",
history_sep = " <FLM_FIX_TOKEN_2><FLM_FIX_TOKEN_1>", dtype = dtype)- ymcui/Chinese-Alpaca-2
| Model | Chinese-Alpaca-2 | Chinese-Alpaca-2-16K |
|---|---|---|
| 7B | ziqingyang/chinese-alpaca-2-7b | ziqingyang/chinese-alpaca-2-7b-16k |
| 13B | ziqingyang/chinese-alpaca-2-13b | ziqingyang/chinese-alpaca-2-13b-16k |
torch2flm.tofile(exportPath, model, tokenizer,
pre_prompt = "<FLM_FIX_TOKEN_1>[INST] <<SYS>>\nYou are a helpful assistant. 你是一个乐于助人的助手。\n<</SYS>>\n\n"
user_role = " ", bot_role = " [/INST]",
history_sep = " <FLM_FIX_TOKEN_2><FLM_FIX_TOKEN_1>", dtype = dtype)- Full
- Delta (需要原始LLaMA)
torch2flm.tofile(exportPath, model, tokenizer,
pre_prompt="The following is a conversation between a human and an AI assistant namely YuLan, developed by GSAI, Renmin University of China. " \
"The AI assistant gives helpful, detailed, and polite answers to the user's questions.\n",
user_role="[|Human|]:", bot_role="\n[|AI|]:", history_sep="\n", dtype=dtype)-
01-ai/Yi-6B-Chat
-
01-ai/Yi-34B-Chat
torch2flm.tofile(exportPath, model, tokenizer, pre_prompt="",
user_role="<|im_start|>user\n", bot_role="<|im_end|><|im_start|>assistant\n", history_sep="<|im_end|>\n", dtype=dtype) torch2flm.tofile(exportPath, model, tokenizer,
pre_prompt="Below is an instruction that describes a task. " \
"Write a response that appropriately completes the request.\n\n",
user_role="### Instruction:\n", bot_role="\n\n### Response:", history_sep="\n", dtype=dtype) torch2flm.tofile(exportPath, model, tokenizer,
pre_prompt="<FLM_FIX_TOKEN_32013> You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, " \
"and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, " \
"and other non-computer science questions, you will refuse to answer.\n",
user_role="### Instruction:\n", bot_role="\n### Response:\n", history_sep="\n<|EOT|>\n", dtype=dtype)