README.md

metadata

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

🧠 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.

📊 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)

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:

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"])