How to use from
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 "pbatra/DeepSeek-R1-Distill-Llama-8B-GGUF" \
    --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": "pbatra/DeepSeek-R1-Distill-Llama-8B-GGUF",
		"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 "pbatra/DeepSeek-R1-Distill-Llama-8B-GGUF" \
        --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": "pbatra/DeepSeek-R1-Distill-Llama-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'
Quick Links

DeepSeek-R1-Distill-Llama-8B

This repository contains quantized versions of the model from the original repository: deepseek-ai/DeepSeek-R1-Distill-Llama-8B.

Name Quantization Method Size (GB)
deepseek-r1-distill-llama-8b.Q8_0.gguf q8_0 7.95
deepseek-r1-distill-llama-8b.Q4_0.gguf q4_0 4.34
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GGUF
Model size
8B params
Architecture
llama
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