From 60fa1cc6350f6663362ed99f2ad39a8f09f36bb7 Mon Sep 17 00:00:00 2001 From: math-zhuxy <235934429@qq.com> Date: Sun, 25 May 2025 18:46:42 +0800 Subject: [PATCH] done --- README.md | 22 +++++++++ model_chat.py | 125 +++++++++++++++++++++----------------------------- 2 files changed, 74 insertions(+), 73 deletions(-) diff --git a/README.md b/README.md index 8ef9609..176b40b 100644 --- a/README.md +++ b/README.md @@ -235,6 +235,18 @@ git clone https://github.com/jingyaogong/minimind.git pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple ``` +因为需要的库较多，建议使用虚拟环境来安装： +```bash +python -m venv .venv +``` + +再下载库： + +```bash +pip install -r requirements.txt +``` + + ### 2.下载模型 到项目根目录 ```bash @@ -243,11 +255,21 @@ git clone https://huggingface.co/jingyaogong/MiniMind2 ### （可选）命令行问答 +如果在第1步中安装了虚拟环境，需要在使用命令行激活虚拟环境： +```bash +.venv/Scripts/activate +``` + ```bash # load=0: load from pytorch model, load=1: load from transformers-hf model python eval_model.py --load 1 --model_mode 2 ``` +或者使用 +```bash +python model_chat.py +``` + ### （可选）启动WebUI ```bash diff --git a/model_chat.py b/model_chat.py index 6a08f19..ed609ac 100644 --- a/model_chat.py +++ b/model_chat.py @@ -1,21 +1,17 @@ +print("开始加载模型") + import argparse import random -import warnings import numpy as np -from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer -from model.model_minimind import MiniMindConfig, MiniMindForCausalLM -from model.model_lora import * +from transformers import AutoTokenizer, TextStreamer, LlamaForCausalLM +import torch -warnings.filterwarnings('ignore') - - -def init_model(args): +def init_model(): transformers_model_path = './MiniMind2' tokenizer = AutoTokenizer.from_pretrained(transformers_model_path) - model = AutoModelForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True) - print(f'MiniMind模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.2f}M(illion)') - return model.eval().to(args.device), tokenizer - + model = LlamaForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True) + print(f'模型参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.2f}M(illion)') + return model.eval().to('cuda' if torch.cuda.is_available() else 'cpu'), tokenizer # 设置可复现的随机种子 def setup_seed(seed): @@ -27,74 +23,57 @@ def setup_seed(seed): torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False +parser = argparse.ArgumentParser(description="Chat with MiniMind") +parser.add_argument('--temperature', default=0.85, type=float) +parser.add_argument('--top_p', default=0.85, type=float) -def main(): - print("开始加载模型") - parser = argparse.ArgumentParser(description="Chat with MiniMind") - parser.add_argument('--lora_name', default='None', type=str) - parser.add_argument('--out_dir', default='out', type=str) - parser.add_argument('--temperature', default=0.85, type=float) - parser.add_argument('--top_p', default=0.85, type=float) - parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str) - # 此处max_seq_len（最大输出长度）并不意味模型具有对应的长文本的性能，仅防止QA出现被截断的问题 - # MiniMind2-moe (145M)：(hidden_size=640, num_hidden_layers=8, use_moe=True) - # MiniMind2-Small (26M)：(hidden_size=512, num_hidden_layers=8) - # MiniMind2 (104M)：(hidden_size=768, num_hidden_layers=16) - parser.add_argument('--hidden_size', default=512, type=int) - parser.add_argument('--num_hidden_layers', default=8, type=int) - parser.add_argument('--max_seq_len', default=8192, type=int) - parser.add_argument('--use_moe', default=False, type=bool) - # 携带历史对话上下文条数 - # history_cnt需要设为偶数，即【用户问题, 模型回答】为1组；设置为0时，即当前query不携带历史上文 - # 模型未经过外推微调时，在更长的上下文的chat_template时难免出现性能的明显退化，因此需要注意此处设置 - parser.add_argument('--history_cnt', default=0, type=int) - parser.add_argument('--load', default=0, type=int, help="0: 原生torch权重，1: transformers加载") - parser.add_argument('--model_mode', default=1, type=int, - help="0: 预训练模型，1: SFT-Chat模型，2: RLHF-Chat模型，3: Reason模型，4: RLAIF-Chat模型") - args = parser.parse_args() +parser.add_argument('--max_seq_len', default=8192, type=int) +args = parser.parse_args() +model, tokenizer = init_model() - model, tokenizer = init_model(args) +streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) - streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True) +print("模型加载完毕") - messages = [] - for prompt in iter(lambda: input('👶: '), ''): - if prompt == "exit": - break - setup_seed(random.randint(0, 2048)) +messages = [] +for prompt in iter(lambda: input('👶: '), ''): + if prompt == "exit": + break - messages.append({"role": "user", "content": prompt}) + elif print == "clear history": + messages = [] + print("成功清除对话历史") + continue + setup_seed(random.randint(0, 2048)) - new_prompt = tokenizer.apply_chat_template( - messages, - tokenize=False, - add_generation_prompt=True - ) if args.model_mode != 0 else (tokenizer.bos_token + prompt) + messages.append({"role": "user", "content": prompt}) - inputs = tokenizer( - new_prompt, - return_tensors="pt", - truncation=True - ).to(args.device) + new_prompt = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True + ) - print('🤖️: ', end='') - generated_ids = model.generate( - inputs["input_ids"], - max_new_tokens=args.max_seq_len, - num_return_sequences=1, - do_sample=True, - attention_mask=inputs["attention_mask"], - pad_token_id=tokenizer.pad_token_id, - eos_token_id=tokenizer.eos_token_id, - streamer=streamer, - top_p=args.top_p, - temperature=args.temperature - ) + inputs = tokenizer( + new_prompt, + return_tensors="pt", + truncation=True + ).to('cuda' if torch.cuda.is_available() else 'cpu') - response = tokenizer.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) - messages.append({"role": "assistant", "content": response}) - print('\n') + print('🤖️: ', end='') + generated_ids = model.generate( + inputs["input_ids"], + max_new_tokens=args.max_seq_len, + num_return_sequences=1, + do_sample=True, + attention_mask=inputs["attention_mask"], + pad_token_id=tokenizer.pad_token_id, + eos_token_id=tokenizer.eos_token_id, + streamer=streamer, + top_p=args.top_p, + temperature=args.temperature + ) - -if __name__ == "__main__": - main() + response = tokenizer.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True) + messages.append({"role": "assistant", "content": response}) + print('\n') \ No newline at end of file