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deepseek-ai
/
DeepSeek-V3.2-Exp

Text Generation
Transformers
Safetensors
deepseek_v32
conversational
fp8
Model card Files Files and versions
xet
Community
36

Instructions to use deepseek-ai/DeepSeek-V3.2-Exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use deepseek-ai/DeepSeek-V3.2-Exp with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-V3.2-Exp")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModelForCausalLM
    model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-V3.2-Exp", dtype="auto")
  • Inference
  • HuggingChat
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • vLLM

    How to use deepseek-ai/DeepSeek-V3.2-Exp with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "deepseek-ai/DeepSeek-V3.2-Exp"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "deepseek-ai/DeepSeek-V3.2-Exp",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/deepseek-ai/DeepSeek-V3.2-Exp
  • SGLang

    How to use deepseek-ai/DeepSeek-V3.2-Exp 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 "deepseek-ai/DeepSeek-V3.2-Exp" \
        --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": "deepseek-ai/DeepSeek-V3.2-Exp",
    		"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 "deepseek-ai/DeepSeek-V3.2-Exp" \
            --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": "deepseek-ai/DeepSeek-V3.2-Exp",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use deepseek-ai/DeepSeek-V3.2-Exp with Docker Model Runner:

    docker model run hf.co/deepseek-ai/DeepSeek-V3.2-Exp
DeepSeek-V3.2-Exp / inference
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  • 8 contributors
History: 2 commits
GeeeekExplorer's picture
GeeeekExplorer
add readme
600c271 8 months ago
  • README.md
    548 Bytes
    add readme 8 months ago
  • config_671B_v3.2.json
    605 Bytes
    add inference demo 8 months ago
  • convert.py
    3.95 kB
    add inference demo 8 months ago
  • generate.py
    7.79 kB
    add inference demo 8 months ago
  • kernel.py
    9.96 kB
    add inference demo 8 months ago
  • model.py
    38 kB
    add inference demo 8 months ago
  • requirements.txt
    70 Bytes
    add inference demo 8 months ago