Text Generation
Transformers
Safetensors
multilingual
phi3
phi
phi4
unsloth
nlp
code
microsoft
math
chat
conversational
custom_code
text-generation-inference
4-bit precision
bitsandbytes
Instructions to use avacaondata/phi4-mini-fixed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use avacaondata/phi4-mini-fixed with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="avacaondata/phi4-mini-fixed", 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("avacaondata/phi4-mini-fixed", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("avacaondata/phi4-mini-fixed", 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 avacaondata/phi4-mini-fixed with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "avacaondata/phi4-mini-fixed" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "avacaondata/phi4-mini-fixed", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/avacaondata/phi4-mini-fixed
- SGLang
How to use avacaondata/phi4-mini-fixed 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 "avacaondata/phi4-mini-fixed" \ --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": "avacaondata/phi4-mini-fixed", "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 "avacaondata/phi4-mini-fixed" \ --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": "avacaondata/phi4-mini-fixed", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio new
How to use avacaondata/phi4-mini-fixed 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 avacaondata/phi4-mini-fixed 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 avacaondata/phi4-mini-fixed to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for avacaondata/phi4-mini-fixed to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="avacaondata/phi4-mini-fixed", max_seq_length=2048, ) - Docker Model Runner
How to use avacaondata/phi4-mini-fixed with Docker Model Runner:
docker model run hf.co/avacaondata/phi4-mini-fixed
| { | |
| "_name_or_path": "unsloth/Phi-4-mini-instruct", | |
| "architectures": [ | |
| "Phi3ForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "configuration_phi3.Phi3Config", | |
| "AutoModelForCausalLM": "modeling_phi3.Phi3ForCausalLM", | |
| "AutoTokenizer": "Xenova/gpt-4o" | |
| }, | |
| "bos_token_id": 199999, | |
| "embd_pdrop": 0.0, | |
| "eos_token_id": 200020, | |
| "full_attn_mod": 1, | |
| "hidden_act": "silu", | |
| "hidden_size": 3072, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 8192, | |
| "interpolate_factor": 1, | |
| "lm_head_bias": false, | |
| "max_position_embeddings": 131072, | |
| "mlp_bias": false, | |
| "model_type": "phi3", | |
| "num_attention_heads": 24, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 8, | |
| "original_max_position_embeddings": 4096, | |
| "pad_token_id": 200029, | |
| "partial_rotary_factor": 0.75, | |
| "quantization_config": { | |
| "_load_in_4bit": true, | |
| "_load_in_8bit": false, | |
| "bnb_4bit_compute_dtype": "bfloat16", | |
| "bnb_4bit_quant_storage": "uint8", | |
| "bnb_4bit_quant_type": "nf4", | |
| "bnb_4bit_use_double_quant": true, | |
| "llm_int8_enable_fp32_cpu_offload": false, | |
| "llm_int8_has_fp16_weight": false, | |
| "llm_int8_skip_modules": [ | |
| "lm_head", | |
| "multi_modal_projector", | |
| "merger", | |
| "modality_projection", | |
| "model.layers.1.mlp", | |
| "model.layers.3.mlp", | |
| "model.layers.30.mlp" | |
| ], | |
| "llm_int8_threshold": 6.0, | |
| "load_in_4bit": true, | |
| "load_in_8bit": false, | |
| "quant_method": "bitsandbytes" | |
| }, | |
| "resid_pdrop": 0.0, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": { | |
| "long_factor": [ | |
| 1, | |
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| "type": "longrope" | |
| }, | |
| "rope_theta": 10000.0, | |
| "sliding_window": 262144, | |
| "tie_word_embeddings": true, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.49.0", | |
| "unsloth_fixed": true, | |
| "use_cache": true, | |
| "vocab_size": 200064 | |
| } | |