Update README.md

README.md

@@ -1,23 +1,48 @@
----
 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
----
+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
+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.
+
 
-# Uploaded  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:
 
-- **Developed by:** inetnuc
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-bnb-4bit
+python
+Kodu kopyala
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+# 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")
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+# 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))