Instructions to use microsoft/phi-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/phi-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="microsoft/phi-1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-1") model = AutoModelForCausalLM.from_pretrained("microsoft/phi-1") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use microsoft/phi-1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "microsoft/phi-1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/phi-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/microsoft/phi-1
- SGLang
How to use microsoft/phi-1 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/phi-1" \ --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/phi-1", "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/phi-1" \ --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/phi-1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use microsoft/phi-1 with Docker Model Runner:
docker model run hf.co/microsoft/phi-1
Upload 4 files
Browse files- README.md +1 -1
- config.json +1 -1
- configuration_phi.py +1 -1
README.md
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---
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inference: false
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license: other
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license_name: microsoft-research-license
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license_link: https://huggingface.co/microsoft/phi-1/resolve/main/Research%20License.docx
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Given the nature of the training data, Phi-1 is best suited for prompts using the code format:
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### Code Format:
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```python
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def print_prime(n):
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"""
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---
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license: other
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license_name: microsoft-research-license
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license_link: https://huggingface.co/microsoft/phi-1/resolve/main/Research%20License.docx
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Given the nature of the training data, Phi-1 is best suited for prompts using the code format:
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### Code Format:
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```python
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def print_prime(n):
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"""
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config.json
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"fused_dense": false,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "phi",
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"n_embd": 2048,
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"n_head": 32,
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"n_head_kv": null,
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"fused_dense": false,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "phi-msft",
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"n_embd": 2048,
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"n_head": 32,
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"n_head_kv": null,
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configuration_phi.py
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class PhiConfig(PretrainedConfig):
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"""Phi configuration."""
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model_type = "phi"
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attribute_map = {
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"max_position_embeddings": "n_positions",
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"hidden_size": "n_embd",
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class PhiConfig(PretrainedConfig):
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"""Phi configuration."""
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model_type = "phi-msft"
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attribute_map = {
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"max_position_embeddings": "n_positions",
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"hidden_size": "n_embd",
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