Instructions to use microsoft/Orca-2-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/Orca-2-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="microsoft/Orca-2-13b")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("microsoft/Orca-2-13b") model = AutoModelForMultimodalLM.from_pretrained("microsoft/Orca-2-13b") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use microsoft/Orca-2-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "microsoft/Orca-2-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/Orca-2-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/microsoft/Orca-2-13b
- SGLang
How to use microsoft/Orca-2-13b 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 "microsoft/Orca-2-13b" \ --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": "microsoft/Orca-2-13b", "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 "microsoft/Orca-2-13b" \ --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": "microsoft/Orca-2-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use microsoft/Orca-2-13b with Docker Model Runner:
docker model run hf.co/microsoft/Orca-2-13b
Update README.md
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README.md
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pipeline_tag: text-generation
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tags:
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- orca
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---
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# Orca 2
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We open-source Orca 2 to encourage further research on the development, evaluation, and alignment of smaller LMs.
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## What is Orca’s intended use(s)?
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+ Orca 2 is built for research purposes only.
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+ The main purpose is to allow the research community to assess its abilities and to provide a foundation for building better frontier models.
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## Model Details
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Orca is a finetuned version of LLAMA-2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. All synthetic training data was filtered using the Azure content filters.
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More details about the model can be found at: LINK to Tech Report
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## License
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The model is licensed under the [Microsoft Research License]().
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Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/llama/license/), Copyright © Meta Platforms, Inc. All Rights Reserved.
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## Uses
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## Bias, Risks, and Limitations
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Orca 2, built upon the LLaMA 2 model family, retains many of its limitations, as well as the
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pipeline_tag: text-generation
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tags:
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- orca
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- orca2
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- microsoft
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---
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# Orca 2
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We open-source Orca 2 to encourage further research on the development, evaluation, and alignment of smaller LMs.
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## What is Orca 2’s intended use(s)?
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+ Orca 2 is built for research purposes only.
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+ The main purpose is to allow the research community to assess its abilities and to provide a foundation for building better frontier models.
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## Model Details
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Orca 2 is a finetuned version of LLAMA-2. Orca 2’s training data is a synthetic dataset that was created to enhance the small model’s reasoning abilities. All synthetic training data was filtered using the Azure content filters.
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More details about the model can be found at: LINK to Tech Report
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Refer to LLaMA-2 for details on model architectures.
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## License
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The model is licensed under the [Microsoft Research License]().
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Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/llama/license/), Copyright © Meta Platforms, Inc. All Rights Reserved.
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## Bias, Risks, and Limitations
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Orca 2, built upon the LLaMA 2 model family, retains many of its limitations, as well as the
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