Instructions to use SkunkworksAI/phi-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SkunkworksAI/phi-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SkunkworksAI/phi-2", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("SkunkworksAI/phi-2", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use SkunkworksAI/phi-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SkunkworksAI/phi-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SkunkworksAI/phi-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SkunkworksAI/phi-2
- SGLang
How to use SkunkworksAI/phi-2 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 "SkunkworksAI/phi-2" \ --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": "SkunkworksAI/phi-2", "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 "SkunkworksAI/phi-2" \ --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": "SkunkworksAI/phi-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SkunkworksAI/phi-2 with Docker Model Runner:
docker model run hf.co/SkunkworksAI/phi-2
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(THIS IS MICROSOFT'S ORIGINAL MODEL, UPLOADED HERE ONLY FOR RESEARCH PURPOSES AND ACCESSIBILITY AS THE AI AZURE STUDIO IS NOT CONVENIENT FOR RESEARCH. RESEARCH ONLY. RESEARCH. RESEARCH, PLEASE DONT SUE US MSFT, THIS IS 100% FOR RESEARCH.)
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**Here is Microsoft's official Phi-2 repo:** https://huggingface.co/microsoft/phi-2
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The phi-2 is a language model with 2.7 billion parameters. The phi-2 model was trained using the same data sources as phi-1, augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). When assessed against benchmarks testing common sense, language understanding, and logical reasoning, the phi-2 showcased a nearly state-of-the-art performance among models with less than 10 billion parameters.
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(THIS IS MICROSOFT'S ORIGINAL MODEL, UPLOADED HERE ONLY FOR RESEARCH PURPOSES AND ACCESSIBILITY AS THE AI AZURE STUDIO IS NOT CONVENIENT FOR RESEARCH. RESEARCH ONLY. RESEARCH. RESEARCH, PLEASE DONT SUE US MSFT, THIS IS 100% FOR RESEARCH.)
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**Here is Microsoft's official Phi-2 repo (float16):** https://huggingface.co/microsoft/phi-2
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The phi-2 is a language model with 2.7 billion parameters. The phi-2 model was trained using the same data sources as phi-1, augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). When assessed against benchmarks testing common sense, language understanding, and logical reasoning, the phi-2 showcased a nearly state-of-the-art performance among models with less than 10 billion parameters.
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