How to use btbtyler09/Llama-3.3-70B-Instruct-gptq-8bit with vLLM:
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "btbtyler09/Llama-3.3-70B-Instruct-gptq-8bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "btbtyler09/Llama-3.3-70B-Instruct-gptq-8bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
docker model run hf.co/btbtyler09/Llama-3.3-70B-Instruct-gptq-8bit
How to use btbtyler09/Llama-3.3-70B-Instruct-gptq-8bit with SGLang:
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "btbtyler09/Llama-3.3-70B-Instruct-gptq-8bit" \ --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": "btbtyler09/Llama-3.3-70B-Instruct-gptq-8bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
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 "btbtyler09/Llama-3.3-70B-Instruct-gptq-8bit" \ --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": "btbtyler09/Llama-3.3-70B-Instruct-gptq-8bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
How to use btbtyler09/Llama-3.3-70B-Instruct-gptq-8bit with Docker Model Runner:
Work in progress gptq quantization of Llama-3.3.
Chat template
Files info
docker model run hf.co/btbtyler09/Llama-3.3-70B-Instruct-gptq-8bit