Instructions to use LinkSoul/Chinese-Llama-2-7b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LinkSoul/Chinese-Llama-2-7b-4bit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="LinkSoul/Chinese-Llama-2-7b-4bit")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LinkSoul/Chinese-Llama-2-7b-4bit")
model = AutoModelForCausalLM.from_pretrained("LinkSoul/Chinese-Llama-2-7b-4bit")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LinkSoul/Chinese-Llama-2-7b-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LinkSoul/Chinese-Llama-2-7b-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LinkSoul/Chinese-Llama-2-7b-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LinkSoul/Chinese-Llama-2-7b-4bit

SGLang

How to use LinkSoul/Chinese-Llama-2-7b-4bit with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "LinkSoul/Chinese-Llama-2-7b-4bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LinkSoul/Chinese-Llama-2-7b-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "LinkSoul/Chinese-Llama-2-7b-4bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LinkSoul/Chinese-Llama-2-7b-4bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LinkSoul/Chinese-Llama-2-7b-4bit with Docker Model Runner:
```
docker model run hf.co/LinkSoul/Chinese-Llama-2-7b-4bit
```

Chinese Llama 2 7B 4bit

全部开源，完全可商用的中文版 Llama2 模型及中英文 SFT 数据集，输入格式严格遵循 llama-2-chat 格式，兼容适配所有针对原版 llama-2-chat 模型的优化。

基础演示

在线试玩

Talk is cheap, Show you the Demo.

Demo 地址 / HuggingFace Spaces
Colab 一键启动 // 正在准备

资源下载

模型下载：Chinese Llama2 Chat Model
4bit量化：Chinese Llama2 4bit Chat Model

我们使用了中英文 SFT 数据集，数据量 1000 万。

数据集：https://huggingface.co/datasets/LinkSoul/instruction_merge_set
训练及推理代码：https://github.com/LinkSoul-AI/Chinese-Llama-2-7b

快速测试

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer

# Original version
# model_path = "LinkSoul/Chinese-Llama-2-7b"
# 4 bit version
model_path = "LinkSoul/Chinese-Llama-2-7b-4bit"


tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
if model_path.endswith("4bit"):
    model = AutoModelForCausalLM.from_pretrained(
            model_path,
            load_in_4bit=True,
            torch_dtype=torch.float16,
            device_map='auto'
        )
else:
    model = AutoModelForCausalLM.from_pretrained(model_path).half().cuda()
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)

instruction = """[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.

            If 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{} [/INST]"""

prompt = instruction.format("用英文回答，什么是夫妻肺片？")
generate_ids = model.generate(tokenizer(prompt, return_tensors='pt').input_ids.cuda(), max_new_tokens=4096, streamer=streamer)

项目协议

Apache-2.0 license

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