Text Generation
Safetensors
PyTorch
Italian
English
mistral
pretrained
causal-lm
minerva
autoround
intel-autoround
autoawq
auto-awq
auto_awq
woq
gptq
intel
conversational
4-bit precision
awq
Instructions to use fbaldassarri/sapienzanlp_Minerva-7B-instruct-v1.0-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/sapienzanlp_Minerva-7B-instruct-v1.0-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/sapienzanlp_Minerva-7B-instruct-v1.0-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/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-int4-gs128-sym", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/fbaldassarri/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-int4-gs128-sym
- SGLang
How to use fbaldassarri/sapienzanlp_Minerva-7B-instruct-v1.0-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/sapienzanlp_Minerva-7B-instruct-v1.0-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/sapienzanlp_Minerva-7B-instruct-v1.0-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/sapienzanlp_Minerva-7B-instruct-v1.0-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/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-int4-gs128-sym", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use fbaldassarri/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-int4-gs128-sym with Docker Model Runner:
docker model run hf.co/fbaldassarri/sapienzanlp_Minerva-7B-instruct-v1.0-autoawq-int4-gs128-sym
- Xet hash:
- 082957a365d5100543a003efa7c05bcb4155cbe062c94d96ca7bfc848011fb86
- Size of remote file:
- 4.47 GB
- SHA256:
- dd0aaa013df42b214f977cfb66cee254041171fdb22915147122ea41959b945a
·
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