How to use from
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 "tibok/baichuan-7B-chatml" \
    --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": "tibok/baichuan-7B-chatml",
		"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 "tibok/baichuan-7B-chatml" \
        --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": "tibok/baichuan-7B-chatml",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'
Quick Links

Baichuan 7B ChatML

介绍 Introduction

baichuan-7B-chatml 是支持多轮对话兼容于 ChatML 的模型。 模型基于 baichuan-7B 微调而成。 baichuan-7B-chatml 模型支持商用。但按照baichuan-7B的要求,如果将baichuan-7B衍生品用作商业用途,需要联系baichuan-7B 的许可方

需要注意:在面对事实性知识任务时,模型可能会生成不正确的信息或者产生不稳定的输出(有时可以返回正确答案,有时不能)。

baichuan-7B-chatml is a model that supports multi-turn dialog and is compatible with ChatML. The model is fine-tuned based on baichuan-7B. baichuan-7B-chatml model supports commercial use. However, according to the requirements of baichuan-7B, if baichuan-7B derivatives are used for commercial purposes, you need to contact baichuan-7B

Note: When dealing with factual knowledge tasks, it may generate incorrect information or unstable output (sometimes it can return the correct answer, sometimes not).

代码示例 Examples

模型在百川的基础上提供了对轮对话的函数供调用。

The model provides a function for multi-turn dialogs.

>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> tokenizer = AutoTokenizer.from_pretrained("tibok/baichuan-7B-chatml", trust_remote_code=True)
>>> model = AutoModelForCausalLM.from_pretrained("tibok/baichuan-7B-chatml", device_map="auto", trust_remote_code=True)
>>> response, history = model.chat(tokenizer, "请以『春天的北京』为题写一首诗歌", history=[])
春天的北京,
花开万丈,
春意盎然,
清风送暖。
<|im_end|>
>>> response, history = model.chat(tokenizer, "能不能再写一首关于香山的?", history=history)
>>> print(response)
香山之巅,
芳草连天。
清泉潺潺,
山峦绵绵。
<|im_end|>

更多细节 Details

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Dataset used to train tibok/baichuan-7B-chatml