---
license: apache-2.0
library_name: transformers
base_model:
- Sao10K/MN-12B-Lyra-v4
datasets:
- jondurbin/gutenberg-dpo-v0.1
---

# Lyra4-Gutenberg-12B - EXL2 8bpw max

This is a 8bpw EXL2 quant of [nbeerbower/Lyra4-Gutenberg-12B](https://huggingface.co/nbeerbower/Lyra4-Gutenberg-12B)

This quant was made using exllamav2-0.2.1 with default dataset. I used a slightly modified quantization script to force use of highest bpw methods for all layers in the model (which is usually "1:8b_128g s4") to ensure max quality.

I also added a small fix in config file to set max default context at 128k as original Mistral-Nemo should have.

I tested this quant shortly in some random RPs (including ones over 8k context) and it seems to work fine.

## Prompt Templates

Uses ChatML or modified mistral format like mentioned in original Lyra v4. I tested it with ChatML.

### Original readme below

---

# Lyra4-Gutenberg-12B

[Sao10K/MN-12B-Lyra-v4](https://huggingface.co/Sao10K/MN-12B-Lyra-v4) finetuned on [jondurbin/gutenberg-dpo-v0.1](https://huggingface.co/datasets/jondurbin/gutenberg-dpo-v0.1).

### Method

ORPO Finetuned using an RTX 3090 + 4060 Ti for 3 epochs.

[Fine-tune Llama 3 with ORPO](https://mlabonne.github.io/blog/posts/2024-04-19_Fine_tune_Llama_3_with_ORPO.html)