Update README.md

README.md

@@ -1,20 +1,67 @@
 ---
+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:
-- gguf
-- llama.cpp
-- unsloth
-
+  - gguf
+  - llama.cpp
+  - unsloth
+  - transformers
+  - math
+  - lora
+  - custom-instruction
 ---
 
-# EfficientMath-AI : GGUF
+# 🧮 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`.
+
+## 📊 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`:
 
-This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
+```python
+from llama_cpp import Llama
 
-**Example usage**:
-- For text only LLMs:    `llama-cli -hf iamabhayaditya/EfficientMath-AI --jinja`
-- For multimodal models: `llama-mtmd-cli -hf iamabhayaditya/EfficientMath-AI --jinja`
+llm = Llama(
+    model_path="Meta-Llama-3.1-8B.Q4_K_M.gguf",
+    n_ctx=2048,
+    n_threads=4
+)
 
-## Available Model files:
-- `Meta-Llama-3.1-8B.Q4_K_M.gguf`
-This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+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"])