Update README.md

README.md

@@ -19,13 +19,14 @@ tags:
 
 This LLAMA-3 model was finetuned to enhance capabilities in text generation for nuclear-related topics. The training was accelerated using [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library, achieving a 2x faster performance.
 
-Finetuning Process
+## Finetuning Process
+
 The model was finetuned using the Unsloth library, leveraging its efficient training capabilities. The process included the following steps:
 
-Data Preparation: Loaded and preprocessed nuclear-related data.
-Model Loading: Utilized unsloth/llama-3-8b-bnb-4bit as the base model.
-LoRA Patching: Applied LoRA (Low-Rank Adaptation) for efficient training.
-Training: Finetuned the model using Hugging Face's TRL library with optimized hyperparameters.
+1. **Data Preparation:** Loaded and preprocessed nuclear-related data.
+2. **Model Loading:** Utilized `unsloth/llama-3-8b-bnb-4bit` as the base model.
+3. **LoRA Patching:** Applied LoRA (Low-Rank Adaptation) for efficient training.
+4. **Training:** Finetuned the model using Hugging Face's TRL library with optimized hyperparameters.
 
 ## Model Details
 
@@ -50,6 +51,3 @@ model = AutoModelForCausalLM.from_pretrained("inetnuc/llama-3-8b-chat-nuclear-lo
 inputs = tokenizer("what is the iaea approach for cyber security?", return_tensors="pt")
 outputs = model.generate(**inputs, max_new_tokens=128)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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-
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