Text Generation
Transformers
PyTorch
private_llm
feature-extraction
custom-code
private-llm
custom_code
Instructions to use MarioBoscoGPU/fqpegaqmsmbd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MarioBoscoGPU/fqpegaqmsmbd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MarioBoscoGPU/fqpegaqmsmbd", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MarioBoscoGPU/fqpegaqmsmbd", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MarioBoscoGPU/fqpegaqmsmbd with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MarioBoscoGPU/fqpegaqmsmbd" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MarioBoscoGPU/fqpegaqmsmbd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MarioBoscoGPU/fqpegaqmsmbd
- SGLang
How to use MarioBoscoGPU/fqpegaqmsmbd 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 "MarioBoscoGPU/fqpegaqmsmbd" \ --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": "MarioBoscoGPU/fqpegaqmsmbd", "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 "MarioBoscoGPU/fqpegaqmsmbd" \ --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": "MarioBoscoGPU/fqpegaqmsmbd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MarioBoscoGPU/fqpegaqmsmbd with Docker Model Runner:
docker model run hf.co/MarioBoscoGPU/fqpegaqmsmbd
- Xet hash:
- f846300f8887fb154f883b8d6fbfb14b94bf39cf494f8a4dc695f1ff0f4bfd8c
- Size of remote file:
- 1.62 kB
- SHA256:
- 6f0a45cc67ce8179e328b1d6ab31d5a3eb35678b832ac97e07ec825546bbfc8c
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