Instructions to use MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- vLLM
How to use MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit
- SGLang
How to use MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit 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 "MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit" \ --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": "MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit", "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 "MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit" \ --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": "MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit with Docker Model Runner:
docker model run hf.co/MLDataScientist/Mistral-Large-Instruct-2407-GPTQ-3bit
File size: 569 Bytes
a84e6a4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"bits": 3,
"group_size": 128,
"sym": true,
"data_type": "int",
"enable_quanted_input": true,
"enable_minmax_tuning": true,
"seqlen": 2048,
"batch_size": 4,
"scale_dtype": "torch.float16",
"lr": 0.001953125,
"minmax_lr": 0.001953125,
"gradient_accumulate_steps": 1,
"iters": 512,
"amp": true,
"nsamples": 256,
"low_gpu_mem_usage": true,
"to_quant_block_names": null,
"enable_norm_bias_tuning": false,
"autoround_version": "0.4.4",
"quant_method": "gptq",
"desc_act": false,
"true_sequential": false,
"damp_percent": 0.01
} |