---
base_model: unsloth/Meta-Llama-3.1-8B-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
datasets:
- Neetree/raw_enko_opus_CCM
---


# KoLama: Fine-Tuned Llama3.1-8B Model

## Overview
KoLama is a fine-tuned version of the **Meta-Llama-3.1-8B-bnb-4bit** model, developed by **Neetree**. This model was trained using the [Unsloth](https://github.com/unslothai/unsloth) library, which significantly accelerated the training process, and Huggingface's TRL (Transformer Reinforcement Learning) library. The model is optimized for text generation tasks and is licensed under **Apache-2.0**.

## Model Details
- **Base Model:** [unsloth/Meta-Llama-3.1-8B-bnb-4bit](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-bnb-4bit)
- **Fine-Tuned by:** Neetree
- **License:** Apache-2.0
- **Language:** English
- **Training Dataset:** [Neetree/raw_enko_opus_CCM](https://huggingface.co/datasets/Neetree/raw_enko_opus_CCM)

## Key Features
- **Efficient Training:** The model was trained 2x faster using Unsloth, making the fine-tuning process more efficient.
- **Text Generation:** Optimized for text generation tasks, leveraging the power of the Llama3.1 architecture.
- **Reinforcement Learning:** Fine-tuned using Huggingface's TRL library, which incorporates reinforcement learning techniques to improve model performance.

## Usage
To use KoLama for text generation, you can load the model using the `transformers` library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Neetree/KoLama"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

input_text = "Once upon a time"
inputs = tokenizer(input_text, return_tensors="pt")

# Generate text
outputs = model.generate(**inputs, max_length=50)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_text)
```

## Training Details
- **Training Speed:** 2x faster training using Unsloth.
- **Fine-Tuning Method:** Supervised Fine-Tuning (SFT) with reinforcement learning via Huggingface's TRL library.
- **Dataset:** The model was fine-tuned on the [Neetree/raw_enko_opus_CCM](https://huggingface.co/datasets/Neetree/raw_enko_opus_CCM) dataset, which contains English-Korean parallel text data.

## License
This model is licensed under the **Apache-2.0** license. For more details, please refer to the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) file.

## Acknowledgments
- **Unsloth:** For providing the tools to accelerate the training process.
- **Huggingface:** For the TRL library and the transformers framework.
- **Meta:** For the original Llama3.1-8B model.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)