0.导入相关依赖
# 升级pip
python -m pip install --upgrade pip# 下载速度慢可以考虑一下更换镜像源。
# -i https://pypi.tuna.tsinghua.edu.cn/simplepip install modelscope==1.9.5
pip install transformers==4.35.2
pip install streamlit==1.24.0
pip install sentencepiece==0.1.99
pip install accelerate==0.24.1
1.通过snapshot_download函数。这是魔塔提供的函数
import torch
from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLM
import os
model_dir = snapshot_download('Shanghai_AI_Laboratory/internlm-20b', cache_dir='/root/zyc/internlm', revision='v1.0.2')
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
torch_dtype=torch.bfloat16
model = AutoModelForCausalLM.from_pretrained(model_dir, torch_dtype=torch.bfloat16, trust_remote_code=True).cuda()
model = model.eval()
inputs = tokenizer(["来到美丽的大自然，我们发现"], return_tensors="pt")
for k,v in inputs.items():inputs[k] = v.cuda()
gen_kwargs = {"max_length": 128, "top_p": 0.8, "temperature": 0.8, "do_sample": True, "repetition_penalty": 1.05}
output = model.generate(**inputs, **gen_kwargs)
output = tokenizer.decode(output[0].tolist(), skip_special_tokens=True)
print(output)2.通过huggingface下载，这是外网
①配置环境（每次使用前都需要）
export HF_ENDPOINT="https://hf-mirror.com"②huggingface-cli使用之前需要登陆
huggingface-cli login
#输入token③下载huggingface插件
pip install -U huggingface_hub④下载加速插件
pip install -U hf-transfer⑤下载模型
huggingface-cli download --resume-download <model-id-or-name> --cache-dir /path/to/cache3.通过大模型做一个flask问答接口
from flask import Flask, request, jsonify
import torch
from modelscope import snapshot_download, AutoTokenizer, AutoModelForCausalLMapp = Flask(__name__)model_dir = 'model_path'
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
torch_dtype = torch.bfloat16
model = AutoModelForCausalLM.from_pretrained(model_dir, torch_dtype=torch.bfloat16, trust_remote_code=True).cuda()
model = model.eval()
app.model_loaded = True@app.route('/generate_text', methods=['POST'])
def generate_text():data = request.get_json() # 假设客户端发送的是JSON格式的数据if not data or 'prompt' not in data:return jsonify({"error": "缺少prompt参数"}), 400prompt = data['prompt']inputs = tokenizer([prompt], return_tensors="pt")for k, v in inputs.items():inputs[k] = v.cuda()gen_kwargs = {"max_length": 128,"top_p": 0.8,"temperature": 0.8,"do_sample": True,"repetition_penalty": 1.05}output = model.generate(**inputs, **gen_kwargs)generated_text = tokenizer.decode(output[0].tolist(), skip_special_tokens=True)return jsonify({"generated_text": generated_text})if __name__ == '__main__':app.run(host='0.0.0.0', port=8848 ,debug=True)4.conda镜像配置
#展示所有镜像源
conda config --show channels
#删除镜像源
conda config --remove channels xxx
#配置清华源
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --set show_channel_urls yes
#单次下载时指定镜像源
conda install -c url 包名
