Text Generation
Safetensors
PyTorch
English
French
mistral
causal-lm
autoround
auto-round
intel-autoround
awq
autoawq
auto-awq
woq
intel
mistralai
conversational
4-bit precision
Instructions to use fbaldassarri/mistralai_Mistral-Small-24B-Instruct-2501-autoawq-int4-gs128-sym with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use fbaldassarri/mistralai_Mistral-Small-24B-Instruct-2501-autoawq-int4-gs128-sym with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fbaldassarri/mistralai_Mistral-Small-24B-Instruct-2501-autoawq-int4-gs128-sym" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fbaldassarri/mistralai_Mistral-Small-24B-Instruct-2501-autoawq-int4-gs128-sym", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/fbaldassarri/mistralai_Mistral-Small-24B-Instruct-2501-autoawq-int4-gs128-sym
- SGLang
How to use fbaldassarri/mistralai_Mistral-Small-24B-Instruct-2501-autoawq-int4-gs128-sym 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 "fbaldassarri/mistralai_Mistral-Small-24B-Instruct-2501-autoawq-int4-gs128-sym" \ --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": "fbaldassarri/mistralai_Mistral-Small-24B-Instruct-2501-autoawq-int4-gs128-sym", "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 "fbaldassarri/mistralai_Mistral-Small-24B-Instruct-2501-autoawq-int4-gs128-sym" \ --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": "fbaldassarri/mistralai_Mistral-Small-24B-Instruct-2501-autoawq-int4-gs128-sym", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use fbaldassarri/mistralai_Mistral-Small-24B-Instruct-2501-autoawq-int4-gs128-sym with Docker Model Runner:
docker model run hf.co/fbaldassarri/mistralai_Mistral-Small-24B-Instruct-2501-autoawq-int4-gs128-sym
- Xet hash:
- 4ef107e4f2c56b6f697308e973f751e8cf458df7f388963db179c63a52fad534
- Size of remote file:
- 4.94 GB
- SHA256:
- 4555b1cfabaf96e0c4e0e60a8eb9a2215987d2cffd28fb12b42e18417be45704
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