Instructions to use microsoft/phi-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/phi-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="microsoft/phi-2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2") model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use microsoft/phi-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "microsoft/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": "microsoft/phi-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/microsoft/phi-2
- SGLang
How to use microsoft/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 "microsoft/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": "microsoft/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 "microsoft/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": "microsoft/phi-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use microsoft/phi-2 with Docker Model Runner:
docker model run hf.co/microsoft/phi-2
Update README.md (#2)
Browse files- Update README.md (93093db3a799f04d6d2a4850749bc5fd6b896781)
Co-authored-by: Mojan Javaheripi <mojanjp@users.noreply.huggingface.co>
README.md
CHANGED
|
@@ -16,6 +16,16 @@ Our model hasn't been fine-tuned through reinforcement learning from human feedb
|
|
| 16 |
|
| 17 |
## Intended Uses
|
| 18 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 19 |
Phi-2 is intended for research purposes only. Given the nature of the training data, the phi-2 model is best suited for prompts using the QA format, the chat format, and the code format.
|
| 20 |
|
| 21 |
#### QA format:
|
|
|
|
| 16 |
|
| 17 |
## Intended Uses
|
| 18 |
|
| 19 |
+
Below are example codes to load phi-2, we support two modes of execution for the model:
|
| 20 |
+
1. loading in fp-16 format with flash-attention support:
|
| 21 |
+
```python
|
| 22 |
+
model = AutoModelForCausalLM.from_pretrained('microsoft/phi-2', torch_dtype='auto', flash_attn=True, flash_rotary=True, fused_dense=True, trust_remote_code=True)
|
| 23 |
+
```
|
| 24 |
+
2. loading in fp-16 without flash-attention
|
| 25 |
+
```python
|
| 26 |
+
model = AutoModelForCausalLM.from_pretrained('microsoft/phi-2', torch_dtype='auto', trust_remote_code=True)
|
| 27 |
+
```
|
| 28 |
+
|
| 29 |
Phi-2 is intended for research purposes only. Given the nature of the training data, the phi-2 model is best suited for prompts using the QA format, the chat format, and the code format.
|
| 30 |
|
| 31 |
#### QA format:
|