Instructions to use internlm/internlm2-chat-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use internlm/internlm2-chat-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="internlm/internlm2-chat-7b", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("internlm/internlm2-chat-7b", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use internlm/internlm2-chat-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "internlm/internlm2-chat-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "internlm/internlm2-chat-7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/internlm/internlm2-chat-7b
- SGLang
How to use internlm/internlm2-chat-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 "internlm/internlm2-chat-7b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "internlm/internlm2-chat-7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "internlm/internlm2-chat-7b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "internlm/internlm2-chat-7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use internlm/internlm2-chat-7b with Docker Model Runner:
docker model run hf.co/internlm/internlm2-chat-7b
Fastchat模型部署推理问题
#11
by wulindong1997 - opened
- fastchat/conversation.py代码中添加模版
# Internlm2-chat template
register_conv_template(
Conversation(
name="internlm2-chat",
system_template="""<|im_start|>system
{system_message}""",
system_message="",
roles=("<|im_start|>user", "<|im_start|>assistant"),
sep_style=SeparatorStyle.INTERN2,
sep="<|im_end|>",
stop_token_ids=[2, 92542],
stop_str='<|im_end|>'
)
)
elif self.sep_style == SeparatorStyle.INTERN2:
ret = "<s>"
for role, message in self.messages:
if message:
ret += role + "\n" + message + self.sep + "\n"
else:
ret += role + "\n"
return ret
- 测试的模版输出
<|im_start|>user
Hello!<|im_end|>
<|im_start|>assistant
Hi!<|im_end|>
<|im_start|>user
How are you?<|im_end|>
<|im_start|>assistant
但是在部署完成之后,调用api response中经常会出现. <eoa> <eoh>等字符,很奇怪。
请求:
{
"model": "internlm2-7b",
"messages": [
{
"role": "user",
"content": "你知道我是谁吗"
}
],
"stream": false
}
回答:
"message": {
"role": "assistant",
"content": " 对不起,我无法识别您的身份。您可以告诉我一些关于您的信息吗?<eoh>\n"
}
求解答