Text Generation
Transformers
Safetensors
English
phi3
phi
nlp
math
code
chat
conversational
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use TOFU-SFT/phi-4-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TOFU-SFT/phi-4-4bit with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TOFU-SFT/phi-4-4bit") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TOFU-SFT/phi-4-4bit") model = AutoModelForCausalLM.from_pretrained("TOFU-SFT/phi-4-4bit") 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 TOFU-SFT/phi-4-4bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TOFU-SFT/phi-4-4bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TOFU-SFT/phi-4-4bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/TOFU-SFT/phi-4-4bit
- SGLang
How to use TOFU-SFT/phi-4-4bit 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 "TOFU-SFT/phi-4-4bit" \ --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": "TOFU-SFT/phi-4-4bit", "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 "TOFU-SFT/phi-4-4bit" \ --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": "TOFU-SFT/phi-4-4bit", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use TOFU-SFT/phi-4-4bit with Docker Model Runner:
docker model run hf.co/TOFU-SFT/phi-4-4bit
| license: mit | |
| license_link: https://huggingface.co/microsoft/phi-4/resolve/main/LICENSE | |
| language: | |
| - en | |
| pipeline_tag: text-generation | |
| tags: | |
| - phi | |
| - nlp | |
| - math | |
| - code | |
| - chat | |
| - conversational | |
| inference: | |
| parameters: | |
| temperature: 0 | |
| widget: | |
| - messages: | |
| - role: user | |
| content: How should I explain the Internet? | |
| library_name: transformers | |
| ## Model Description | |
| - **Developed by**: Microsoft Research | |
| - **Model type**: Causal Language Models | |
| - **License**: MIT | |
| ## Bias, Risks, and Limitations | |
| Warning: This model may produce harmful content | |
| ## Citation | |
| ``` | |
| @article{abdin2024phi, | |
| title={Phi-4 technical report}, | |
| author={Abdin, Marah and Aneja, Jyoti and Behl, Harkirat and Bubeck, S{\'e}bastien and Eldan, Ronen and Gunasekar, Suriya and Harrison, Michael and Hewett, Russell J and Javaheripi, Mojan and Kauffmann, Piero and others}, | |
| journal={arXiv preprint arXiv:2412.08905}, | |
| year={2024} | |
| } | |