Instructions to use yujiepan/phi-4-tiny-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use yujiepan/phi-4-tiny-random with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="yujiepan/phi-4-tiny-random", 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("yujiepan/phi-4-tiny-random", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("yujiepan/phi-4-tiny-random", trust_remote_code=True, device_map="auto") 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 Settings
- vLLM
How to use yujiepan/phi-4-tiny-random with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "yujiepan/phi-4-tiny-random" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "yujiepan/phi-4-tiny-random", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/yujiepan/phi-4-tiny-random
- SGLang
How to use yujiepan/phi-4-tiny-random 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 "yujiepan/phi-4-tiny-random" \ --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": "yujiepan/phi-4-tiny-random", "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 "yujiepan/phi-4-tiny-random" \ --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": "yujiepan/phi-4-tiny-random", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use yujiepan/phi-4-tiny-random with Docker Model Runner:
docker model run hf.co/yujiepan/phi-4-tiny-random
| library_name: transformers | |
| pipeline_tag: text-generation | |
| inference: true | |
| widget: | |
| - text: Hello! | |
| example_title: Hello world | |
| group: Python | |
| This model is for debugging. It is randomly initialized with the config from [microsoft/phi-4](https://huggingface.co/microsoft/phi-4) but is of smaller size. | |
| Codes: | |
| ```python | |
| import os | |
| import torch | |
| import transformers | |
| from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer, | |
| GenerationConfig, pipeline, set_seed) | |
| model_id = "microsoft/phi-4" | |
| repo_id = "yujiepan/phi-4-tiny-random" | |
| save_path = f"/tmp/{repo_id}" | |
| config = AutoConfig.from_pretrained(model_id, trust_remote_code=True) | |
| config.hidden_size = 16 | |
| config.intermediate_size = 32 | |
| config.num_attention_heads = 2 | |
| config.num_hidden_layers = 2 | |
| config.num_key_value_heads = 1 | |
| tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) | |
| tokenizer.save_pretrained(save_path) | |
| model = AutoModelForCausalLM.from_config( | |
| config, torch_dtype=torch.bfloat16, | |
| trust_remote_code=True, | |
| ) | |
| model.generation_config = GenerationConfig.from_pretrained( | |
| model_id, trust_remote_code=True | |
| ) | |
| set_seed(42) | |
| with torch.no_grad(): | |
| for name, p in sorted(model.named_parameters()): | |
| torch.nn.init.normal_(p, 0, 0.5) | |
| print(name, p.shape) | |
| model.save_pretrained(save_path) | |
| pipe = pipeline("text-generation", model=save_path, device="cuda", | |
| trust_remote_code=True, max_new_tokens=20) | |
| print(pipe("Hello World!")) | |
| ``` | |