Update README.md

README.md

@@ -1,48 +1,53 @@
+---
 base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
 language:
-
-en
+- en
 license: apache-2.0
 tags:
-text-generation-inference
-transformers
-unsloth
-llama
-trl
-sft
-
-LLAMA-3.1 8B Chat Nuclear Model
-Developed by: inetnuc
-License: apache-2.0
-Finetuned from model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
-This LLAMA-3.1 model was finetuned to enhance capabilities in text generation for nuclear-related topics. The training was accelerated using Unsloth and Huggingface's TRL library, achieving a 2x faster performance.
-
-Finetuning Process
+- text-generation-inference
+- transformers
+- unsloth
+- llama
+- gguf
+---
+
+# LLAMA-3.1 8B Chat Nuclear Model
+
+- **Developed by:** inetnuc
+- **License:** apache-2.0
+- **Finetuned from model:** unsloth/Meta-Llama-3.1-8B-bnb-4bit
+
+This LLAMA-3.1 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
 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/Meta-Llama-3.1-8B-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
+
+- **Base Model:** `unsloth/llama-3.1-8b-bnb-4bit`
+- **Language:** English (`en`)
+- **License:** Apache-2.0
+
+## Usage
+
+### Loading the Model
 
-Model Details
-Base Model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
-Language: English (en)
-License: Apache-2.0
-Usage
-Loading the Model
 You can load the model and tokenizer using the following code snippet:
 
-python
-Kodu kopyala
+```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 # Load the tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("inetnuc/llama-3-8b-chat-nuclear")
-model = AutoModelForCausalLM.from_pretrained("inetnuc/llama-3-8b-chat-nuclear")
+tokenizer = AutoTokenizer.from_pretrained("inetnuc/Llama-3.1-8B-bnb-4bit-chat-nuclear-lora")
+model = AutoModelForCausalLM.from_pretrained("inetnuc/Llama-3.1-8B-bnb-4bit-chat-nuclear-lora")
 
 # Example of generating text
 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))
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+