Instructions to use tclf90/qwen2.5-72b-instruct-gptq-int3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use tclf90/qwen2.5-72b-instruct-gptq-int3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tclf90/qwen2.5-72b-instruct-gptq-int3" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tclf90/qwen2.5-72b-instruct-gptq-int3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tclf90/qwen2.5-72b-instruct-gptq-int3
- SGLang
How to use tclf90/qwen2.5-72b-instruct-gptq-int3 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 "tclf90/qwen2.5-72b-instruct-gptq-int3" \ --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": "tclf90/qwen2.5-72b-instruct-gptq-int3", "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 "tclf90/qwen2.5-72b-instruct-gptq-int3" \ --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": "tclf90/qwen2.5-72b-instruct-gptq-int3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use tclf90/qwen2.5-72b-instruct-gptq-int3 with Docker Model Runner:
docker model run hf.co/tclf90/qwen2.5-72b-instruct-gptq-int3
- Xet hash:
- b252ce7d1e91c102cfca226ea20e4098e05772d81b4abc43fd6fb0969848a279
- Size of remote file:
- 4.96 GB
- SHA256:
- e3b9c0d24983b3ff5fe896c9bfd7bd60f43519b1f11a8e19cb4b077824ee1412
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