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empirischtech
/
DeepSeek-R1-Distill-Qwen-32B-gptq-4bit

Text Generation
Safetensors
English
qwen2
gptq
int4
quantized
qlora
medical
medqa
biology
chemistry
finance
legal
climate
reasoning
4-bit precision
conversational
Eval Results (legacy)
Model card Files Files and versions
xet
Community
1

Instructions to use empirischtech/DeepSeek-R1-Distill-Qwen-32B-gptq-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Local Apps
  • vLLM

    How to use empirischtech/DeepSeek-R1-Distill-Qwen-32B-gptq-4bit with vLLM:

    Install from pip and serve model
    # Install vLLM from pip:
    pip install vllm
    # Start the vLLM server:
    vllm serve "empirischtech/DeepSeek-R1-Distill-Qwen-32B-gptq-4bit"
    # Call the server using curl (OpenAI-compatible API):
    curl -X POST "http://localhost:8000/v1/chat/completions" \
    	-H "Content-Type: application/json" \
    	--data '{
    		"model": "empirischtech/DeepSeek-R1-Distill-Qwen-32B-gptq-4bit",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
    Use Docker
    docker model run hf.co/empirischtech/DeepSeek-R1-Distill-Qwen-32B-gptq-4bit
  • SGLang

    How to use empirischtech/DeepSeek-R1-Distill-Qwen-32B-gptq-4bit 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 "empirischtech/DeepSeek-R1-Distill-Qwen-32B-gptq-4bit" \
        --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": "empirischtech/DeepSeek-R1-Distill-Qwen-32B-gptq-4bit",
    		"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 "empirischtech/DeepSeek-R1-Distill-Qwen-32B-gptq-4bit" \
            --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": "empirischtech/DeepSeek-R1-Distill-Qwen-32B-gptq-4bit",
    		"messages": [
    			{
    				"role": "user",
    				"content": "What is the capital of France?"
    			}
    		]
    	}'
  • Docker Model Runner

    How to use empirischtech/DeepSeek-R1-Distill-Qwen-32B-gptq-4bit with Docker Model Runner:

    docker model run hf.co/empirischtech/DeepSeek-R1-Distill-Qwen-32B-gptq-4bit
DeepSeek-R1-Distill-Qwen-32B-gptq-4bit
19.4 GB
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  • 1 contributor
History: 9 commits
rwmasood's picture
rwmasood
Update README.md
e1e7ec5 verified about 1 month ago
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