updated readme

README.md

@@ -20,7 +20,7 @@ datasets:
 This model is a fine-tuned version of [unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit).
 It has been trained using [TRL](https://github.com/huggingface/trl). 
 
-This model is fine-tuned for detecting vulnerabilities in code.
+This model is fine-tuned for detecting vulnerabilities in code with the Chain-of-Thought method.
 
 Dataset Used: [Mackerel2/cybernative_code_vulnerability_cot](https://huggingface.co/datasets/Mackerel2/cybernative_code_vulnerability_cot)
 
@@ -29,7 +29,7 @@ Dataset Used: [Mackerel2/cybernative_code_vulnerability_cot](https://huggingface
 - Use for code vulnerability analysis
 - Use for general code related question answering (use without given chat template)
 
-## Use model with a chat template for Chain of Thoughts response
+## Use model with a chat template for Chain-of-Thought response
 ```python
 import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
@@ -102,7 +102,7 @@ print("<think>\n" + output)
 
 ## Training procedure
 
-This model was trained with SFT Trainer of trl library. I have Leveraged Unsloth’s FastLanguageModel with 4-bit quantization and smart gradient checkpointing to fit within consumer GPUs. I designed prompts where reasoning is enclosed in <think>...</think> and final answers in <answer>...</answer>. This helps guide the model to reason step-by-step before answering. I have used SFTTrainer from HuggingFace TRL with LoRA + 8bit optimizer + cosine LR scheduling. Evaluation is performed every 50 steps. I have used PEFT/LoRA for efficient fine-tuning.
+This model was trained with SFT Trainer of trl library. I have Leveraged Unsloth’s FastLanguageModel with 4-bit quantization and smart gradient checkpointing to fit within consumer GPUs. I designed prompts where reasoning is enclosed in \<think>...\</think> and final answers in \<answer>...\</answer>. This helps guide the model to reason step-by-step before answering. I have used SFTTrainer from HuggingFace TRL with LoRA + 8bit optimizer + cosine LR scheduling. Evaluation is performed every 50 steps. I have used PEFT/LoRA for efficient fine-tuning.
 
 | Parameter                   | Value                               |
 |----------------------------|-------------------------------------|