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
Download pytorch_model-00003-of-00004.bin from internlm/internlm3-8b-instruct-awq: direct link, hf CLI and curl.
- Browser
- Download file 1.09 GB
-
https://huggingface.co/internlm/internlm3-8b-instruct-awq/resolve/main/pytorch_model-00003-of-00004.bin
- Command line
-
hf download hf://internlm/internlm3-8b-instruct-awq/pytorch_model-00003-of-00004.bin
-
curl -L -o pytorch_model-00003-of-00004.bin https://huggingface.co/internlm/internlm3-8b-instruct-awq/resolve/main/pytorch_model-00003-of-00004.bin
1.09 GB
- Xet hash:
- 38a634c139664c9323b0e09faa0edf80745da4bb1f46f397a296e2319a14780f
- Size of remote file:
- 1.09 GB
- SHA256:
- 511eab2c579e16436dbf44fda740bf9d46783859e18d3c87e12031c4eb36a2b2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.