Instructions to use internlm/internlm3-8b-instruct-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use internlm/internlm3-8b-instruct-awq with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "internlm/internlm3-8b-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": "internlm/internlm3-8b-instruct-awq", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/internlm/internlm3-8b-instruct-awq
- SGLang
How to use internlm/internlm3-8b-instruct-awq 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 "internlm/internlm3-8b-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": "internlm/internlm3-8b-instruct-awq", "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 "internlm/internlm3-8b-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": "internlm/internlm3-8b-instruct-awq", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use internlm/internlm3-8b-instruct-awq with Docker Model Runner:
docker model run hf.co/internlm/internlm3-8b-instruct-awq
- Xet hash:
- f65abf6a4d482148afe5da968b33c9f6d3f92940c9535255d0dbed177bb00b14
- Size of remote file:
- 2.48 MB
- SHA256:
- bcacff3229854f5103ee7a85473a30ca9a8b3a68f3aae9b7479574b23ac2256b
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