Text Generation
Transformers
Safetensors
English
Chinese
nanbeige
llm
conversational
custom_code
Eval Results
Instructions to use Nanbeige/Nanbeige4.2-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nanbeige/Nanbeige4.2-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Nanbeige/Nanbeige4.2-3B", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Nanbeige/Nanbeige4.2-3B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Nanbeige/Nanbeige4.2-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Nanbeige/Nanbeige4.2-3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Nanbeige/Nanbeige4.2-3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Nanbeige/Nanbeige4.2-3B
- SGLang
How to use Nanbeige/Nanbeige4.2-3B 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 "Nanbeige/Nanbeige4.2-3B" \ --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": "Nanbeige/Nanbeige4.2-3B", "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 "Nanbeige/Nanbeige4.2-3B" \ --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": "Nanbeige/Nanbeige4.2-3B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Nanbeige/Nanbeige4.2-3B with Docker Model Runner:
docker model run hf.co/Nanbeige/Nanbeige4.2-3B
GPQA复现上出现问题
#30
by BShooter - opened
您好!非常感谢您的工作!我使用您提到的分支构建了环境,并使用temperature0.6+top-p0.95+256的方式尝试复现GPQA,但每次都只能达到70-75分的水平,且存在重复率较高,速度较慢的问题,请问您这边可以提供您使用的prompt模板或者更多细节吗?
我们这边按照model card中的配置可以正常复现GPQA结果。目前的结果差异,应该是评测或推理配置没有完全对齐。
首先请按照model card中的SGLang版本安装环境,并在启动模型服务时配置--reasoning-parser nanbeige。
测试时无system prompt(默认system prompt)、无few-shot。模板如下:
Answer the following multiple choice question. The last line of your response should be of the following format: 'Answer: $LETTER' (without quotes) where LETTER is one of ABCD. Think step by step before answering.
{Question}
A) {A}
B) {B}
C) {C}
D) {D}
请求参数如下:
{
"messages": [
{
"role": "user",
"content": "<上面的完整 prompt>"
}
],
"temperature": 0.6,
"top_p": 0.95,
"top_k": 20,
"max_tokens": 98304,
"stream": false,
"chat_template_kwargs": {
"enable_thinking": true
}
}
好的!十分感谢您的回复,我再根据您提供的配置进行尝试。