done
math-zhuxy
parent
9e35e5348f
commit
60fa1cc635
22
README.md
22
README.md
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@ -235,6 +235,18 @@ git clone https://github.com/jingyaogong/minimind.git
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pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
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```
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因为需要的库较多，建议使用虚拟环境来安装：
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```bash
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python -m venv .venv
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```
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再下载库：
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```bash
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pip install -r requirements.txt
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```
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### 2.下载模型
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到项目根目录
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```bash
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@ -243,11 +255,21 @@ git clone https://huggingface.co/jingyaogong/MiniMind2
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### （可选）命令行问答
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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：
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```bash
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.venv/Scripts/activate
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```
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```bash
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# load=0: load from pytorch model, load=1: load from transformers-hf model
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python eval_model.py --load 1 --model_mode 2
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```
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或者使用
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```bash
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python model_chat.py
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```
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### （可选）启动WebUI
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```bash
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@ -1,21 +1,17 @@
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print("开始加载模型")
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import argparse
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import random
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import warnings
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import numpy as np
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from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer
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from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
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from model.model_lora import *
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from transformers import AutoTokenizer, TextStreamer, LlamaForCausalLM
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import torch
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warnings.filterwarnings('ignore')
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def init_model(args):
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def init_model():
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transformers_model_path = './MiniMind2'
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tokenizer = AutoTokenizer.from_pretrained(transformers_model_path)
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model = AutoModelForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True)
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print(f'MiniMind模型参数量: {sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.2f}M(illion)')
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return model.eval().to(args.device), tokenizer
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model = LlamaForCausalLM.from_pretrained(transformers_model_path, trust_remote_code=True)
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print(f'模型参数量：{sum(p.numel() for p in model.parameters() if p.requires_grad) / 1e6:.2f}M(illion)')
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return model.eval().to('cuda' if torch.cuda.is_available() else 'cpu'), tokenizer
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# 设置可复现的随机种子
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def setup_seed(seed):
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@ -27,40 +23,27 @@ def setup_seed(seed):
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torch.backends.cudnn.deterministic = True
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torch.backends.cudnn.benchmark = False
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def main():
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print("开始加载模型")
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parser = argparse.ArgumentParser(description="Chat with MiniMind")
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parser.add_argument('--lora_name', default='None', type=str)
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parser.add_argument('--out_dir', default='out', type=str)
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parser.add_argument('--temperature', default=0.85, type=float)
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parser.add_argument('--top_p', default=0.85, type=float)
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parser.add_argument('--device', default='cuda' if torch.cuda.is_available() else 'cpu', type=str)
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# 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
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# MiniMind2-moe (145M)：(hidden_size=640, num_hidden_layers=8, use_moe=True)
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# MiniMind2-Small (26M)：(hidden_size=512, num_hidden_layers=8)
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# MiniMind2 (104M)：(hidden_size=768, num_hidden_layers=16)
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parser.add_argument('--hidden_size', default=512, type=int)
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parser.add_argument('--num_hidden_layers', default=8, type=int)
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parser.add_argument('--max_seq_len', default=8192, type=int)
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parser.add_argument('--use_moe', default=False, type=bool)
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# 携带历史对话上下文条数
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# 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
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# 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
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parser.add_argument('--history_cnt', default=0, type=int)
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parser.add_argument('--load', default=0, type=int, help="0: 原生torch权重，1: transformers加载")
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parser.add_argument('--model_mode', default=1, type=int,
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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")
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args = parser.parse_args()
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model, tokenizer = init_model(args)
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parser.add_argument('--max_seq_len', default=8192, type=int)
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args = parser.parse_args()
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model, tokenizer = init_model()
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streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
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print("模型加载完毕")
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messages = []
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for prompt in iter(lambda: input('👶: '), ''):
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if prompt == "exit":
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break
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elif print == "clear history":
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messages = []
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print("成功清除对话历史")
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continue
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setup_seed(random.randint(0, 2048))
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messages.append({"role": "user", "content": prompt})
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@ -69,13 +52,13 @@ def main():
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messages,
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tokenize=False,
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add_generation_prompt=True
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) if args.model_mode != 0 else (tokenizer.bos_token + prompt)
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)
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inputs = tokenizer(
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new_prompt,
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return_tensors="pt",
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truncation=True
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).to(args.device)
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).to('cuda' if torch.cuda.is_available() else 'cpu')
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print('🤖️: ', end='')
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generated_ids = model.generate(
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response = tokenizer.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
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messages.append({"role": "assistant", "content": response})
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print('\n')
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if __name__ == "__main__":
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main()
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