MagicHub/Chinese-llama2-alpaca-7b

This language model was finetuned with a dataset of 48k machine-translated Chinese instructions. For dataset description, inference examples and other details, see: https://github.com/magichub-opensource/CLAM-Conversational-Language-AI-from-MagicData.

模型推理

• 单卡加载一个模型需要15G显存。
• 本地测试环境：py310-torch1.13.1-cuda11.6-cudnn8

Web Demo

我们使用 text-generation-webui 开源项目搭建的 demo 进行推理，得到文档中的对比样例。该demo支持在网页端切换模型、调整多种常见参数等。

实验环境：py310-torch1.13.1-cuda11.6-cudnn8

git clone https://github.com/oobabooga/text-generation-webui.git
cd text-generation-webui
pip install -r requirements.txt

# 建议使用软链接将模型绝对路径链至 `./models`。也可以直接拷贝进去。
ln -s ${model_dir_absolute_path} models/${model_name}

# 启动服务
python server.py --model ${model_name} --listen --listen-host 0.0.0.0 --listen-port ${port}

如果服务正常启动，就可以通过该端口访问服务了 ${server_ip}:${port}

Inference script

See https://github.com/magichub-opensource/CLAM-Conversational-Language-AI-from-MagicData/blob/master/inference.py

import os,sys,argparse
# os.environ['CUDA_VISIBLE_DEVICES'] = '1'
import torch
import re
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

# modelpath = 'models/Chinese-llama2-alpaca-7b' # local path
modelpath = 'MagicHub/Chinese-llama2-alpaca-7b' # huggingface repo

print(f'model path: {modelpath}')
model = AutoModelForCausalLM.from_pretrained(modelpath, device_map="cuda:0", torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained(modelpath, use_fast=False)

prompt = "歌剧和京剧的区别是什么？\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda:0")
generate_ids = model.generate(
        inputs.input_ids, do_sample=True, max_new_tokens=1024, top_k=10, top_p=0.1, temperature=0.5, repetition_penalty=1.18,
        eos_token_id=2, bos_token_id=1, pad_token_id=0, typical_p=1.0,encoder_repetition_penalty=1,
        )
response = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
cleaned_response = re.sub('^'+prompt,'', response)
print(f'输入：\n{prompt}\n')
print(f"输出：\n{cleaned_response}\n")