Update README.md

README.md

@@ -20,4 +20,56 @@ base_model:
 
 ![FastLlama-Logo](FastLlama.png)
 
-You can use ChatML & Alpaca format.
+You can use ChatML & Alpaca format.
+
+**Overview**
+FastLlama is a highly optimized version of the Llama-3.2-1B-Instruct model. Designed for superior performance in constrained environments, it combines speed, compactness, and high accuracy. This version has been fine-tuned using the MetaMathQA-50k section of the HuggingFaceTB/smoltalk dataset to enhance its mathematical reasoning and problem-solving abilities.
+
+**Features**
+Lightweight and Fast: Optimized to deliver Llama-class capabilities with reduced computational overhead.
+Fine-Tuned for Math Reasoning: Utilizes MetaMathQA-50k for better handling of complex mathematical problems and logical reasoning tasks.
+Instruction-Tuned: Pre-trained on instruction-following tasks, making it robust in understanding and executing detailed queries.
+Versatile Use Cases: Suitable for educational tools, tutoring systems, or any application requiring mathematical reasoning.
+
+**Performance Highlights**
+Smaller Footprint: The model delivers comparable results to larger counterparts while operating efficiently on smaller hardware.
+Enhanced Accuracy: Demonstrates improved performance on mathematical QA benchmarks.
+Instruction Adherence: Retains high fidelity in understanding and following user instructions, even for complex queries.
+
+**Loading the Model**
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the model and tokenizer
+model_name = "your-hf-username/fastllama-3.2-1b-instruct-metamathqa"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Example usage
+input_text = "Solve for x: 2x + 3 = 7"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+print(response)
+```
+
+**Dataset**
+Dataset: MetaMathQA-50k
+The MetaMathQA-50k subset of HuggingFaceTB/smoltalk was selected for fine-tuning due to its focus on mathematical reasoning, multi-step problem-solving, and logical inference. The dataset includes:
+
+Algebraic problems
+Geometric reasoning tasks
+Statistical and probabilistic questions
+Logical deduction problems
+
+**Model Fine-Tuning**
+Fine-tuning was conducted using the following configuration:
+
+Learning Rate: 2e-4
+Epochs: 1
+Optimizer: AdamW
+Framework: Unsloth
+
+**License**
+This model is licensed under the Apache 2.0 License. See the LICENSE file for details.