madhu-sagar123/math-llama-problemMath-Llama3.5K

🧮 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, fine-tuned on 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
Finetuned Dataset	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)

./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

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:

@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.