README.md

---
library_name: transformers
license: llama3.1
datasets:
- jondurbin/gutenberg-dpo-v0.1
- nbeerbower/gutenberg2-dpo
- jondurbin/truthy-dpo-v0.1
- kyujinpy/orca_math_dpo
- antiven0m/physical-reasoning-dpo
base_model:
- mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated
---
![image/png](https://huggingface.co/nbeerbower/Llama3.1-Allades-8B/resolve/main/allades.png?download=true)

# Llama3.1-Allades-8B

Allades finetunes abliterated Llama 3.1 with 5 datasets to improve creative writing, reasoning, and roleplay.

## Datasets

- [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1)
- [nbeerbower/gutenberg2-dpo](https://huggingface.co/datasets/nbeerbower/gutenberg2-dpo)
- [jondurbin/truthy-dpo-v0.1](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)
- [kyujinpy/orca_math_dpo](https://huggingface.co/datasets/kyujinpy/orca_math_dpo)
- [antiven0m/physical-reasoning-dpo](https://huggingface.co/datasets/antiven0m/physical-reasoning-dpo)

## Training

[ORPO tuned](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html) for 1 epoch with 2x RTX 3090 (sponsored by [Schneewolf Labs](https://schneewolflabs.com)).

Data was prepared with [Llama 3.1 Instruct](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/).