Instructions to use microsoft/Phi-3-medium-128k-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/Phi-3-medium-128k-instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="microsoft/Phi-3-medium-128k-instruct", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-medium-128k-instruct", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-medium-128k-instruct", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use microsoft/Phi-3-medium-128k-instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "microsoft/Phi-3-medium-128k-instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/Phi-3-medium-128k-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/microsoft/Phi-3-medium-128k-instruct
- SGLang
How to use microsoft/Phi-3-medium-128k-instruct 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-3-medium-128k-instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/Phi-3-medium-128k-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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-3-medium-128k-instruct" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "microsoft/Phi-3-medium-128k-instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use microsoft/Phi-3-medium-128k-instruct with Docker Model Runner:
docker model run hf.co/microsoft/Phi-3-medium-128k-instruct
about multilingual label
It is stated to be a multilingual model, but there is no specific mention of which languages are included, so I am curious.
Can you share which languages are included and to what extent for each country?
Thank you for your interest! The Phi models are trained primarily on English text. In the Small and Medium we have some multilingual training data. We see the Small and Medium models are doing well on some languages such as German, Spanish, French, Japanese, Russian, Chinese.
did you have any Vietnamese?
Thank you for the clarification on the multilingual capabilities of the Phi models. It's great to know they perform well in several languages. I appreciate your help!
Small and Medium have some Vietnamese data, but the Phi model family are trained primarily on English. We are eager to learn more from the community on language-specific finetuning experiments.