README.md

---
base_model: meta-llama/Meta-Llama-3.1-8B
library_name: peft
license: llama3.1
datasets:
- openai/gsm8k
language:
- en
metrics:
- accuracy
- perplexity
pipeline_tag: text-generation
tags:
- llama.cpp
- unsloth
- transformers
- math
- custom-instruction
- LoRA
---

# 🧮 EfficientMath-AI (Llama 3.1 8B)

## 📌 Project Overview
EfficientMath-AI is a parameter-efficient fine-tuned (PEFT) version of Meta's **Llama-3.1-8B**, specifically optimized to solve multi-step, grade-school math word problems. It was trained using LoRA (Low-Rank Adaptation) and compressed into a 4-bit quantized GGUF format, allowing it to perform high-level mathematical reasoning efficiently on standard CPU hardware.

**Creator:** Abhay Aditya
**Live Interactive Demo:** [EfficientMath-AI Web App](https://huggingface.co/spaces/iamabhayaditya/EfficientMath-AI)

## 🧠 Model Details
* **Base Model:** `meta-llama/Meta-Llama-3.1-8B`
* **Fine-Tuning Method:** LoRA (Rank = 16, Alpha = 16) via Unsloth
* **Dataset:** GSM8K (Grade School Math 8K)
* **Quantization:** `Q4_K_M` (4-bit GGUF)
* **Parameters:** 8 Billion
* **Deployment Context:** Designed for high-speed, CPU-only inference via `llama.cpp`.
* 📊 **Model Impact Note:** While the sidebar tracks the rolling 30-day window,EfficientMath-AI has accumulated 85+ total lifetime downloads since its release on March 24, 2026!

## 📊 Evaluation & Performance
The model was evaluated against a rigorous test split of the GSM8K dataset, focusing on strict numeric extraction and step-by-step reasoning coherence.
* **Overall Accuracy:** 66%
* **Training Hardware:** Single NVIDIA T4 GPU (Free Tier)
* **Inference Hardware Requirement:** ~8GB RAM (Basic CPU)

![Research Graphs](research_graphs.jpg)

### Diagnostic Insights:
1. **Perplexity:** The model exhibits a tightly clustered, low perplexity distribution (between 2.5 and 4.0), demonstrating high confidence and fluency in generating mathematical syntax.
2. **Complexity Ceiling:** The model achieves near 80% accuracy on short word problems, maintaining a concise and highly accurate "Chain of Thought" without hallucinating verbose responses. Like many 8B class models, accuracy scales inversely with prompt length on highly complex, multi-paragraph logic puzzles.

## 💻 Usage Example (Python)
If you wish to run this model locally, you can use `llama-cpp-python`:

```python
from llama_cpp import Llama

llm = Llama(
    model_path="Meta-Llama-3.1-8B.Q4_K_M.gguf",
    n_ctx=2048,
    n_threads=4
)

output = llm(
    "Below is a math word problem. Solve it step by step and provide the final answer.\n\n### Problem:\nIf the cost of 18 apples is 90 rupees, what is the cost of 24 apples?\n\n### Solution:\n",
    max_tokens=256,
    temperature=0.2,
    stop=["<|eot_id|>"]
)
print(output["choices"][0]["text"])