Instructions to use baichuan-inc/Baichuan2-7B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use baichuan-inc/Baichuan2-7B-Chat with Transformers:

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

pipe = pipeline("text-generation", model="baichuan-inc/Baichuan2-7B-Chat", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("baichuan-inc/Baichuan2-7B-Chat", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use baichuan-inc/Baichuan2-7B-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "baichuan-inc/Baichuan2-7B-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "baichuan-inc/Baichuan2-7B-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/baichuan-inc/Baichuan2-7B-Chat

SGLang

How to use baichuan-inc/Baichuan2-7B-Chat 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 "baichuan-inc/Baichuan2-7B-Chat" \
    --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": "baichuan-inc/Baichuan2-7B-Chat",
		"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 "baichuan-inc/Baichuan2-7B-Chat" \
        --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": "baichuan-inc/Baichuan2-7B-Chat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use baichuan-inc/Baichuan2-7B-Chat with Docker Model Runner:
```
docker model run hf.co/baichuan-inc/Baichuan2-7B-Chat
```

NormHead 中的分支判断

by JaheimLee - opened Sep 6, 2023

Discussion

JaheimLee

Sep 6, 2023

您好，请教NormHead的forward中为什么采用三个分支来生成norm_weight啊，直接norm_weight = nn.functional.normalize(self.weight)会有什么问题吗？另外，forward中存在nn.Parameter会使deepspeed报错，可以避免这个问题吗？感谢！

wuzhiying2023

Sep 6, 2023

您好，请教NormHead的forward中为什么采用三个分支来生成norm_weight啊，直接norm_weight = nn.functional.normalize(self.weight)会有什么问题吗？另外，forward中存在nn.Parameter会使deepspeed报错，可以避免这个问题吗？感谢！

训练的时候直接norm_weight = nn.functional.normalize(self.weight)是可以的，这么做主要是为了减少计算，提高性能。如果是训练，你可以改成直接normalize的方式也行。

JaheimLee

Sep 6, 2023

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