Instructions to use omurberaisik/holocomnb7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use omurberaisik/holocomnb7 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.3") model = PeftModel.from_pretrained(base_model, "omurberaisik/holocomnb7") - Transformers
How to use omurberaisik/holocomnb7 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="omurberaisik/holocomnb7")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("omurberaisik/holocomnb7", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use omurberaisik/holocomnb7 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "omurberaisik/holocomnb7" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "omurberaisik/holocomnb7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/omurberaisik/holocomnb7
- SGLang
How to use omurberaisik/holocomnb7 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 "omurberaisik/holocomnb7" \ --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": "omurberaisik/holocomnb7", "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 "omurberaisik/holocomnb7" \ --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": "omurberaisik/holocomnb7", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use omurberaisik/holocomnb7 with Docker Model Runner:
docker model run hf.co/omurberaisik/holocomnb7
- Xet hash:
- 19d622ad0ed2e880277fd3feeb028f482b43a3043881e566a3c30ccf8808ad30
- Size of remote file:
- 5.2 kB
- SHA256:
- 7d729270ed2e068bcbc6a8ebc8663affb36d0a8e4deed27551b5a376b20b5072
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