Instructions to use rhysjones/phi-2-orange with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rhysjones/phi-2-orange with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rhysjones/phi-2-orange", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("rhysjones/phi-2-orange", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rhysjones/phi-2-orange with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rhysjones/phi-2-orange" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rhysjones/phi-2-orange", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/rhysjones/phi-2-orange
- SGLang
How to use rhysjones/phi-2-orange 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 "rhysjones/phi-2-orange" \ --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": "rhysjones/phi-2-orange", "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 "rhysjones/phi-2-orange" \ --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": "rhysjones/phi-2-orange", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use rhysjones/phi-2-orange with Docker Model Runner:
docker model run hf.co/rhysjones/phi-2-orange
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README.md
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license: mit
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license: mit
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# Phi-2 Orange
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A two-step finetune of Phi-2.
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First using a collection of broad training data:
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- [Open-Orca/SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup)
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- [migtissera/Synthia-v1.3](https://huggingface.co/datasets/migtissera/Synthia-v1.3)
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- [LDJnr/Verified-Camel](https://huggingface.co/datasets/LDJnr/Verified-Camel)
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- [LDJnr/Pure-Dove](https://huggingface.co/datasets/LDJnr/Pure-Dove)
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- [LDJnr/Capybara](https://huggingface.co/datasets/LDJnr/Capybara)
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- [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA)
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And then a DPO finetune using:
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- [Intel/orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
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- [argilla/ultrafeedback-binarized-preferences-cleaned](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences-cleaned)
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# Initial Evals
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- ARC: 62.29
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- TruthfulQA: 49.85
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