Instructions to use microsoft/phi-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/phi-4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="microsoft/phi-4") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-4") model = AutoModelForCausalLM.from_pretrained("microsoft/phi-4") 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]:])) - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use microsoft/phi-4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "microsoft/phi-4" # 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-4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/microsoft/phi-4
- SGLang
How to use microsoft/phi-4 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-4" \ --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-4", "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-4" \ --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-4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use microsoft/phi-4 with Docker Model Runner:
docker model run hf.co/microsoft/phi-4
Restore <|endoftext|> (100257) as a stop token in generation_config
Summary
Set generation_config.json eos_token_id back to [100257, 100265], restoring<|endoftext|> (100257) alongside <|im_end|> (100265) as a stop token. This reverts
only the eos_token_id change from #21 while keeping that PR's other improvements
(chat template fix, pad_token -> <|dummy_85|>).
Problem
With eos_token_id set to 100265 only, the model can fail to stop: it produces a
correct answer and then continues emitting unrelated text until max_tokens is reached
(reported as "random tokens until the max token limit").
Root cause
phi-4 ends many (typically terse) assistant turns with <|endoftext|> (100257) rather
than <|im_end|> (100265). Since #21, generation_config.json only lists 100265, so
when the model emits 100257 no stop criterion fires; decoding runs past the turn
boundary (the following <|im_start|>user<|im_sep|> gets stripped on output, leaving a
stray user) and free-runs until the token cap.
Evidence
- Reproduced with bare HF
model.generate()at temperature 0 (greedy). Identical on
transformers 4.47 and 5.13, so it is not a transformers-version issue. - Reproduced with vLLM 0.18.0 and 0.24.0 — byte-identical output to HF.
- Next-token distribution right after "The capital of France is Paris.":
P(<|endoftext|>=100257) = 0.72vsP(<|im_end|>=100265) = 0.27, so greedy decoding
deterministically selects the token the current config does not stop on. - With
eos_token_id = [100257, 100265], generation stops cleanly on all three engines.
Scope
Only generation_config.json is changed. config.json and the tokenizer files are left
as-is; this is the minimal change that both transformers and vLLM honor for stopping.