base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
language:

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
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.


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
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")

# 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))