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Qwen
/
Qwen-14B-Chat

Text Generation
Transformers
Safetensors
Chinese
English
qwen
custom_code
Model card Files Files and versions
xet
Community
18

Instructions to use Qwen/Qwen-14B-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Qwen/Qwen-14B-Chat with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="Qwen/Qwen-14B-Chat", trust_remote_code=True)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-14B-Chat", trust_remote_code=True, dtype="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use Qwen/Qwen-14B-Chat with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "Qwen/Qwen-14B-Chat"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "Qwen/Qwen-14B-Chat",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
    Use Docker
    docker model run hf.co/Qwen/Qwen-14B-Chat
  • SGLang

    How to use Qwen/Qwen-14B-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 "Qwen/Qwen-14B-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": "Qwen/Qwen-14B-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 "Qwen/Qwen-14B-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": "Qwen/Qwen-14B-Chat",
    		"prompt": "Once upon a time,",
    		"max_tokens": 512,
    		"temperature": 0.5
    	}'
  • Docker Model Runner

    How to use Qwen/Qwen-14B-Chat with Docker Model Runner:

    docker model run hf.co/Qwen/Qwen-14B-Chat
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Request: DOI

#19 opened 5 months ago by
Coranoire

如何控制输出长度呢

#18 opened about 2 years ago by
ckllt

网址拼接的问题

2
#17 opened about 2 years ago by
MaxGavin

chat_stream 的使用

#16 opened over 2 years ago by
jayceeNice

Can you please submit this to leaderboard?

6
#15 opened over 2 years ago by
gblazex

Max sequence length config

1
#14 opened over 2 years ago by
ihungalexhsu

如何让模型输出的结果,严格按照定义的json结构进行输出?

👍 2
1
#12 opened over 2 years ago by
tang0430

Copyright Concerns on Commercial Use of Distilled GPT-3.5 Data - GPT-3.5 蒸馏数据用于商业用途的版权疑虑

#4 opened over 2 years ago by
JosephusCheung

支持ollama运行吗?

#3 opened over 2 years ago by
esqer
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