---
base_model: unsloth/phi-4-unsloth-bnb-4bit
tags:
  - text-generation
  - transformers
  - unsloth
  - llama
  - trl
license: apache-2.0
language:
  - en
---

# 🧮 Phi-4 Math Reasoning Model (LoRA Finetuned)

## 📌 Model Overview
This model is a **LoRA fine-tuned version** of [unsloth/phi-4-unsloth-bnb-4bit](https://huggingface.co/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](https://github.com/unslothai/unsloth) for **2x faster training** using Hugging Face’s [TRL](https://huggingface.co/docs/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:
```bash
pip install unsloth transformers accelerate bitsandbytes
```



🖥️ Usage (Colab / Local GPU)

```bash
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
- [Unsloth](https://github.com/unslothai/unsloth)  
- [Transformers](https://huggingface.co/docs/transformers)  
- [TRL](https://huggingface.co/docs/trl)  
- [Kaggle Notebook](https://www.kaggle.com/code/yaminh/finetuning-a-llm-for-math-reasoning-sbs)

---