README.md

---
license: apache-2.0
language:
  - en
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - llama
  - gguf
library_name: transformers
pipeline_tag: text-generation
datasets:
  - Rimyy/problemMath-Llama3.5K
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
model_name: llama-3.2-3b-instruct-bnb-4bit-math-gguf
---

# 🧮 LLaMA 3.2 3B Instruct (Unsloth 4-bit) — Finetuned on Rimyy/problemMath-Llama3.5K (GGUF)

This model is a **4-bit GGUF** variant of [`unsloth/llama-3.2-3b-instruct-bnb-4bit`](https://huggingface.co/unsloth/llama-3.2-3b-instruct-bnb-4bit), fine-tuned on [`Rimyy/problemMath-Llama3.5K`](https://huggingface.co/datasets/Rimyy/problemMath-Llama3.5K), a high-quality dataset of math reasoning and problem-solving questions. The model is tailored for **math instruction**, **step-by-step reasoning**, and educational applications.

> 🚨 Designed to reason, not just regurgitate. Small model, big brain.

---

## 🧠 Model Details

| Feature           | Value                                                                 |
|-------------------|-----------------------------------------------------------------------|
| Base              | [`unsloth/llama-3.2-3b-instruct-bnb-4bit`](https://huggingface.co/unsloth/llama-3.2-3b-instruct-bnb-4bit) |
| Finetuned Dataset | [`Rimyy/problemMath-Llama3.5K`](https://huggingface.co/datasets/Rimyy/problemMath-Llama3.5K)              |
| Quantization      | 4-bit GGUF (compatible with llama.cpp/text-generation-webui)         |
| Format            | GGUF                                                                 |
| Language          | English                                                              |
| Instruction Tuned | ✅ Yes                                                               |

---

## 📚 Dataset: `Rimyy/problemMath-Llama3.5K`

- ~3.5K math word problems and reasoning tasks  
- Emphasizes chain-of-thought (CoT) explanations  
- Covers arithmetic, algebra, and word problems  
- Aligns with OpenAI-style "question → step-by-step answer" format  

---

## 🔧 Quick Usage Example (llama.cpp)

```bash
./main -m llama-3.2-3b-math.gguf   --prompt "### Question: What is the value of x if x + 3 = 7?
### Answer:"
```

Expected output:
```
To solve for x, subtract 3 from both sides of the equation:
x + 3 = 7
x = 7 - 3
x = 4

Answer: 4
```

---

## 🧪 Usage in Python

```python
from llama_cpp import Llama

llm = Llama(
    model_path="llama-3.2-3b-instruct-math.q4_K.gguf",
    n_ctx=2048,
    n_gpu_layers=32,  # adjust based on your GPU
)

prompt = (
    "### Question: If a rectangle has length 10 and width 5, what is its area?
"
    "### Answer:"
)
response = llm(prompt)
print(response["choices"][0]["text"])
```

---

## 📦 Applications

- 🤖 Math tutoring agents  
- 📚 AI-driven educational platforms  
- 🧩 RAG pipelines for mathematical queries  
- 📝 Automated solution generators  

---

## ⚠️ Limitations

- Occasional step hallucinations  
- Not optimized for LaTeX-heavy symbolic math  
- May struggle on very long multi-step problems  

---

## 📊 Qualitative Benchmark

| Task Type         | Performance        |
|-------------------|--------------------|
| Simple Arithmetic | ✅ Excellent        |
| One-Step Algebra  | ✅ Strong           |
| Multi-Step CoT    | ⚠️ Good (some drift)|
| Logic Puzzles     | ⚠️ Mixed            |

> 📌 Quantitative benchmarks forthcoming.

---

## 🔗 Citation

If you use this model, please cite:

```bibtex
@misc{rimyy2025math,
  author = {Rimyy},
  title = {ProblemMath-Llama3.5K: A Dataset for Math Problem Solving},
  year = {2025},
  url = {https://huggingface.co/datasets/Rimyy/problemMath-Llama3.5K}
}
```

---

## 🙌 Acknowledgements

- **Meta** for LLaMA 3.  
- **Unsloth** for the 4-bit instruct base.  
- **Rimyy** for an excellent math dataset.  
- **llama.cpp & GGUF** community for stellar tooling.

---

🔢 *Small enough to run on your laptop, smart enough to teach algebra.*