---
language: en
license: apache-2.0
tags:
- llama
- transformer
- 8b
- 4bit
- instruction-tuning
- conversational
pipeline_tag: text-generation
inference: false
model_creator: 0xroyce
model_type: LLaMA
---

# Valkyrie-Llama-3.1-8B-bnb-4bit

Valkyrie-Llama-3.1-8B-bnb-4bit is an advanced language model fine-tuned on a mixture of diverse and high-quality datasets to achieve a balance between performance and efficiency. Utilizing the LLaMA architecture and optimized with 4-bit quantization, this model is designed for resource-constrained environments while maintaining strong performance on natural language processing tasks.

![Valkyrie Banner](https://huggingface.co/0xroyce/Valkyrie-Llama-3.1-8B-bnb-4bit/resolve/main/valkyrie.jpeg)

## Model Details

- **Model Type**: LLaMA
- **Model Size**: 8 Billion Parameters
- **Quantization**: 4-bit (bnb, bitsandbytes)
- **Architecture**: Transformer-based
- **Creator**: [0xroyce](https://huggingface.co/0xroyce)
- **License**: [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0)

## Training

Valkyrie-Llama-3.1-8B-bnb-4bit was fine-tuned on a curated dataset containing diverse textual data, including but not limited to:

- Conversational data
- Instruction-following tasks
- Diverse web content
- Academic articles

The fine-tuning process leveraged Unsloth.ai for optimizing model performance, ensuring a well-balanced approach to both accuracy and efficiency. The 4-bit quantization allows for deployment in environments with limited computational resources without a significant loss in model performance.

## Intended Use

This model is intended for a variety of natural language processing tasks, including but not limited to:

- **Conversational AI**: Ideal for creating chatbots and virtual assistants.
- **Text Generation**: Can be used to generate coherent and contextually relevant text.
- **Instruction Following**: Capable of understanding and following detailed instructions.

## Performance

While specific benchmark scores for Valkyrie-Llama-3.1-8B-bnb-4bit are not provided, it is designed to perform competitively with other models in the 8B parameter range. The 4-bit quantization is particularly useful for deployment in resource-limited settings, providing a good trade-off between model size and performance.

## Limitations

Despite its strengths, the Valkyrie-Llama-3.1-8B-bnb-4bit model has some limitations:

- **Biases**: As with any large language model, it may generate biased or inappropriate content depending on the input.
- **Inference Speed**: Although optimized with 4-bit quantization, there may still be latency in real-time applications depending on the deployment environment.
- **Context Length**: The model has a limited context window, which can affect its ability to handle long documents or multi-turn conversations effectively.

## How to Use

You can load and use the model with the following code:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("0xroyce/Valkyrie-Llama-3.1-8B-bnb-4bit")
model = AutoModelForCausalLM.from_pretrained("0xroyce/Valkyrie-Llama-3.1-8B-bnb-4bit")

input_text = "Your text here"
input_ids = tokenizer(input_text, return_tensors="pt").input_ids

output = model.generate(input_ids, max_length=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

## Ethical Considerations

The Valkyrie-Llama-3.1-8B-bnb-4bit model, like all large language models, can generate text that may be biased or harmful. Users should apply appropriate content filtering and moderation when deploying this model in public-facing applications. Additionally, developers are encouraged to fine-tune the model further to align it with specific ethical guidelines or usage policies.

## Citation

If you use this model in your research or applications, please cite it as follows:

```bibtex
@misc{0xroyce2024valkyrie,
  author = {0xroyce},
  title = {Valkyrie-Llama-3.1-8B-bnb-4bit},
  year = {2024},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/0xroyce/Valkyrie-Llama-3.1-8B-bnb-4bit}},
}
```

## Acknowledgements

Special thanks to the open-source community and contributors who made this model possible.