Instructions to use MoxoffSrL/Volare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MoxoffSrL/Volare with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MoxoffSrL/Volare")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MoxoffSrL/Volare") model = AutoModelForCausalLM.from_pretrained("MoxoffSrL/Volare", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MoxoffSrL/Volare with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MoxoffSrL/Volare" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MoxoffSrL/Volare", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MoxoffSrL/Volare
- SGLang
How to use MoxoffSrL/Volare 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 "MoxoffSrL/Volare" \ --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": "MoxoffSrL/Volare", "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 "MoxoffSrL/Volare" \ --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": "MoxoffSrL/Volare", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MoxoffSrL/Volare with Docker Model Runner:
docker model run hf.co/MoxoffSrL/Volare
Download model-00001-of-00004.safetensors from MoxoffSrL/Volare: direct link, hf CLI and curl.
- Browser
- Download file 5 GB
-
https://huggingface.co/MoxoffSrL/Volare/resolve/main/model-00001-of-00004.safetensors
- Command line
-
hf download hf://MoxoffSrL/Volare/model-00001-of-00004.safetensors
-
curl -L -o model-00001-of-00004.safetensors https://huggingface.co/MoxoffSrL/Volare/resolve/main/model-00001-of-00004.safetensors
5 GB
- Xet hash:
- 1d8ca715433c7faeec88fe588c152aaa8c302b4b256c301e5e5004ae3ca11f98
- Size of remote file:
- 5 GB
- SHA256:
- 5a2c343e2dd85c1567956d04a6341b57b4deb1fddca9a6ac8488602965215e7a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.