Instructions to use btbtyler09/Devstral-Small-2507-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps
- vLLM
How to use btbtyler09/Devstral-Small-2507-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "btbtyler09/Devstral-Small-2507-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "btbtyler09/Devstral-Small-2507-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/btbtyler09/Devstral-Small-2507-AWQ
- SGLang
How to use btbtyler09/Devstral-Small-2507-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 "btbtyler09/Devstral-Small-2507-AWQ" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "btbtyler09/Devstral-Small-2507-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "btbtyler09/Devstral-Small-2507-AWQ" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "btbtyler09/Devstral-Small-2507-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use btbtyler09/Devstral-Small-2507-AWQ with Docker Model Runner:
docker model run hf.co/btbtyler09/Devstral-Small-2507-AWQ
| { | |
| "dim": 5120, | |
| "n_layers": 40, | |
| "head_dim": 128, | |
| "hidden_dim": 32768, | |
| "n_heads": 32, | |
| "n_kv_heads": 8, | |
| "rope_theta": 1000000000.0, | |
| "norm_eps": 1e-05, | |
| "vocab_size": 131072 | |
| } |