Instructions to use Open-Orca/OpenOrca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Open-Orca/OpenOrca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Open-Orca/OpenOrca")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Open-Orca/OpenOrca") model = AutoModelForCausalLM.from_pretrained("Open-Orca/OpenOrca", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Open-Orca/OpenOrca with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Open-Orca/OpenOrca" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Open-Orca/OpenOrca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Open-Orca/OpenOrca
- SGLang
How to use Open-Orca/OpenOrca 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 "Open-Orca/OpenOrca" \ --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": "Open-Orca/OpenOrca", "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 "Open-Orca/OpenOrca" \ --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": "Open-Orca/OpenOrca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Open-Orca/OpenOrca with Docker Model Runner:
docker model run hf.co/Open-Orca/OpenOrca
Update README.md
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README.md
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# Evaluation
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We have evaluated
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Our average performance for BigBench-Hard: 0.3753
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So we got 60% of the improvement with 6% of the data!
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## BigBench-Hard Performance
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 Evals once we receive them.
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year = {2023},
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publisher = {HuggingFace},
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journal = {HuggingFace repository},
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howpublished = {\url{https://https://huggingface.co/Open-Orca/
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}
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```
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```bibtex
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# Evaluation
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We have evaluated OpenOrca-Preview1-13B on hard reasoning tasks from BigBench-Hard and AGIEval as outlined in the Orca paper.
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Our average performance for BigBench-Hard: 0.3753
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So we got 60% of the improvement with 6% of the data!
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## BigBench-Hard Performance
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## AGIEval Performance
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We will report our results on [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Evals once we receive them.
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year = {2023},
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publisher = {HuggingFace},
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journal = {HuggingFace repository},
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howpublished = {\url{https://https://huggingface.co/Open-Orca/OpenOrca-Preview1-13B},
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}
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```
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```bibtex
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