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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.*
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