Instructions to use RobinMillford/phi-4-math-reasoning-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RobinMillford/phi-4-math-reasoning-lora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RobinMillford/phi-4-math-reasoning-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("RobinMillford/phi-4-math-reasoning-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RobinMillford/phi-4-math-reasoning-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RobinMillford/phi-4-math-reasoning-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RobinMillford/phi-4-math-reasoning-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RobinMillford/phi-4-math-reasoning-lora

SGLang

How to use RobinMillford/phi-4-math-reasoning-lora 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 "RobinMillford/phi-4-math-reasoning-lora" \
    --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": "RobinMillford/phi-4-math-reasoning-lora",
		"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 "RobinMillford/phi-4-math-reasoning-lora" \
        --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": "RobinMillford/phi-4-math-reasoning-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use RobinMillford/phi-4-math-reasoning-lora 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 RobinMillford/phi-4-math-reasoning-lora 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 RobinMillford/phi-4-math-reasoning-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RobinMillford/phi-4-math-reasoning-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="RobinMillford/phi-4-math-reasoning-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use RobinMillford/phi-4-math-reasoning-lora with Docker Model Runner:
```
docker model run hf.co/RobinMillford/phi-4-math-reasoning-lora
```

🧮 Phi-4 Math Reasoning Model (LoRA Finetuned)

📌 Model Overview

This model is a LoRA fine-tuned version of unsloth/phi-4-unsloth-bnb-4bit.
It has been fine-tuned specifically for math reasoning tasks, capable of solving step-by-step arithmetic, algebra, and logic problems.

The base model is Phi-4, a 14B-parameter LLaMA variant optimized with Unsloth for 2x faster training using Hugging Face’s TRL library.
This version uses bnb-4bit quantization, making it memory efficient and suitable for single-GPU setups such as Tesla T4 (16GB) or consumer GPUs.

⚡ Key Features

🧠 Fine-tuned for math reasoning and step-by-step solutions
⚡ Efficient: 4-bit quantized, runs on a single GPU or even CPU (slower)
🚀 Trained with Unsloth + TRL for fast and memory-efficient fine-tuning
📚 Based on Phi-4 (14B LLaMA model)

📥 Installation

Ensure you have the latest versions of the required libraries:

pip install unsloth transformers accelerate bitsandbytes

🖥️ Usage (Colab / Local GPU)

import torch
from unsloth import FastLanguageModel
from transformers import TextStreamer

# Load the LoRA fine-tuned model
model_name = "RobinMillford/phi-4-math-reasoning-lora"
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name=model_name,
    max_seq_length=2048,
    dtype=torch.float16,   # fp16 recommended for GPU
    load_in_4bit=True,     # load in 4-bit quantized mode
    device_map="auto"      # automatically place layers on GPU/CPU
)

# Prepare for inference
FastLanguageModel.for_inference(model)

# Example: Generate a step-by-step solution
streamer = TextStreamer(tokenizer)
inputs = tokenizer(
    "Solve step by step: Q: What is 24 * 17 ? A:",
    return_tensors="pt"
).to("cuda")

_ = model.generate(**inputs, streamer=streamer, max_new_tokens=500)

📊 Example Output

Prompt:

Solve step by step: Q: What is 45 + 67 ?

Response:

Step 1: Add the ones digits: 5 + 7 = 12. Write down 2 and carry over 1. Step 2: Add the tens digits plus carry: 4 + 6 + 1 = 11. Step 3: Combine the results: 112. Answer: 112

⚠️ Disclaimer

This model is intended for research and educational purposes only.

It may not be fully accurate for complex math reasoning tasks. Always verify critical calculations independently.

❤️ Made With

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for RobinMillford/phi-4-math-reasoning-lora

Base model

microsoft/phi-4

Quantized

unsloth/phi-4-unsloth-bnb-4bit

Finetuned

(350)

this model