Update README.md

README.md

@@ -22,26 +22,29 @@ base_model:
 
 You can use ChatML & Alpaca format.
 
-**Overview**
+**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**
+**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**
+**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**
+**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"
+model_name = "suayptalha/FastLlama-3.2-1B-Instruct"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name)
 
@@ -54,7 +57,8 @@ response = tokenizer.decode(outputs[0], skip_special_tokens=True)
 print(response)
 ```
 
-**Dataset**
+**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:
 
@@ -63,7 +67,8 @@ Geometric reasoning tasks
 Statistical and probabilistic questions
 Logical deduction problems
 
-**Model Fine-Tuning**
+**Model Fine-Tuning:**
+
 Fine-tuning was conducted using the following configuration:
 
 Learning Rate: 2e-4
@@ -71,5 +76,6 @@ Epochs: 1
 Optimizer: AdamW
 Framework: Unsloth
 
-**License**
+**License:**
+
 This model is licensed under the Apache 2.0 License. See the LICENSE file for details.