Instructions to use MagicHub/Chinese-llama2-CLAM-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MagicHub/Chinese-llama2-CLAM-7b with Transformers:

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

pipe = pipeline("text-generation", model="MagicHub/Chinese-llama2-CLAM-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MagicHub/Chinese-llama2-CLAM-7b")
model = AutoModelForCausalLM.from_pretrained("MagicHub/Chinese-llama2-CLAM-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MagicHub/Chinese-llama2-CLAM-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MagicHub/Chinese-llama2-CLAM-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MagicHub/Chinese-llama2-CLAM-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MagicHub/Chinese-llama2-CLAM-7b

SGLang

How to use MagicHub/Chinese-llama2-CLAM-7b 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 "MagicHub/Chinese-llama2-CLAM-7b" \
    --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": "MagicHub/Chinese-llama2-CLAM-7b",
		"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 "MagicHub/Chinese-llama2-CLAM-7b" \
        --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": "MagicHub/Chinese-llama2-CLAM-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MagicHub/Chinese-llama2-CLAM-7b with Docker Model Runner:
```
docker model run hf.co/MagicHub/Chinese-llama2-CLAM-7b
```

Chinese-llama2-CLAM-7b / README.md

MagicHub

Update README.md

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	---
	license: llama2
	language:
	- zh
	tags:
	- text-generation-inference
	---

	This language model was finetuned with a dataset of 52k Chinese instructions. The dataset is called MagicData-CLAM and was originally generated in Chinese (instead of translated from English). 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](https://github.com/oobabooga/text-generation-webui/tree/main) 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-CLAM-7b' # local path
	modelpath = 'MagicHub/Chinese-llama2-CLAM-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")
	```