How to use Orion-zhen/OpenCoder-8B-Instruct-AWQ with vLLM:
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Orion-zhen/OpenCoder-8B-Instruct-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Orion-zhen/OpenCoder-8B-Instruct-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
docker model run hf.co/Orion-zhen/OpenCoder-8B-Instruct-AWQ
How to use Orion-zhen/OpenCoder-8B-Instruct-AWQ with SGLang:
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Orion-zhen/OpenCoder-8B-Instruct-AWQ" \ --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": "Orion-zhen/OpenCoder-8B-Instruct-AWQ", "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 "Orion-zhen/OpenCoder-8B-Instruct-AWQ" \ --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": "Orion-zhen/OpenCoder-8B-Instruct-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
How to use Orion-zhen/OpenCoder-8B-Instruct-AWQ with Docker Model Runner:
# Install vLLM from pip: pip install vllm
# Start the vLLM server: vllm serve "Orion-zhen/OpenCoder-8B-Instruct-AWQ"
# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Orion-zhen/OpenCoder-8B-Instruct-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'
AWQ quantization of infly/OpenCoder-8B-Instruct, with group size 128, GEMM
Chat template
Files info
Base model
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "Orion-zhen/OpenCoder-8B-Instruct-AWQ"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Orion-zhen/OpenCoder-8B-Instruct-AWQ", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'