Text Generation
Transformers
Safetensors
English
llama
text-generation-inference
unsloth
trl
conversational
Instructions to use ykarout/phi4-nvidia-coder-fp16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ykarout/phi4-nvidia-coder-fp16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ykarout/phi4-nvidia-coder-fp16") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ykarout/phi4-nvidia-coder-fp16") model = AutoModelForCausalLM.from_pretrained("ykarout/phi4-nvidia-coder-fp16") 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 ykarout/phi4-nvidia-coder-fp16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ykarout/phi4-nvidia-coder-fp16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ykarout/phi4-nvidia-coder-fp16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ykarout/phi4-nvidia-coder-fp16
- SGLang
How to use ykarout/phi4-nvidia-coder-fp16 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 "ykarout/phi4-nvidia-coder-fp16" \ --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": "ykarout/phi4-nvidia-coder-fp16", "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 "ykarout/phi4-nvidia-coder-fp16" \ --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": "ykarout/phi4-nvidia-coder-fp16", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use ykarout/phi4-nvidia-coder-fp16 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ykarout/phi4-nvidia-coder-fp16 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ykarout/phi4-nvidia-coder-fp16 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ykarout/phi4-nvidia-coder-fp16 to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="ykarout/phi4-nvidia-coder-fp16", max_seq_length=2048, ) - Docker Model Runner
How to use ykarout/phi4-nvidia-coder-fp16 with Docker Model Runner:
docker model run hf.co/ykarout/phi4-nvidia-coder-fp16
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| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100335": { | |
| "content": "<|dummy_71|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100336": { | |
| "content": "<|dummy_72|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100337": { | |
| "content": "<|dummy_73|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100338": { | |
| "content": "<|dummy_74|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100339": { | |
| "content": "<|dummy_75|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100340": { | |
| "content": "<|dummy_76|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100341": { | |
| "content": "<|dummy_77|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100342": { | |
| "content": "<|dummy_78|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100343": { | |
| "content": "<|dummy_79|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100344": { | |
| "content": "<|dummy_80|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100345": { | |
| "content": "<|dummy_81|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100346": { | |
| "content": "<|dummy_82|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100347": { | |
| "content": "<|dummy_83|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100348": { | |
| "content": "<|dummy_84|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100349": { | |
| "content": "<|dummy_85|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100350": { | |
| "content": "<|dummy_86|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "100351": { | |
| "content": "<|dummy_87|>", | |
| "lstrip": true, | |
| "normalized": false, | |
| "rstrip": true, | |
| "single_word": false, | |
| "special": true | |
| } | |
| }, | |
| "bos_token": "<|endoftext|>", | |
| "chat_template": "{% for message in messages %}{% if (message['role'] == 'system') %}{{'<|im_start|>system<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'user') %}{{'<|im_start|>user<|im_sep|>' + message['content'] + '<|im_end|>'}}{% elif (message['role'] == 'assistant') %}{{'<|im_start|>assistant<|im_sep|>' + message['content'] + '<|im_end|>'}}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant<|im_sep|>' }}{% endif %}", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|im_end|>", | |
| "extra_special_tokens": {}, | |
| "model_max_length": 16384, | |
| "pad_token": "<|dummy_87|>", | |
| "padding_side": "left", | |
| "tokenizer_class": "GPT2Tokenizer", | |
| "unk_token": "�" | |
| } | |