---
license: cc-by-nc-4.0
base_model: athirdpath/BigMistral-11b
tags:
- generated_from_trainer
model-index:
- name: qlora
  results: []
language:
- en
pipeline_tag: text-generation  
---

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)

<p align="center"><font size="5"> <b>Regret: Should have not targeted Q, V, K, O; as those are less impactful for "healing" but more impactful on performance otherwise. Still works great!</b> </font></p>

# qlora

This model is a fine-tuned version of [athirdpath/BigMistral-11b](https://huggingface.co/athirdpath/BigMistral-11b) on the athirdpath/Merge_Glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9174

<p align="center"><font size="7"> <b>Before and After Example</b></font></p>
<p align="center"><font size="4"> <b>Example model is athirdpath/CleverMage-11b</b></font></p>
<p align="center"><font size="5"> <b>Example with LoRA (min_p, alpaca)</b></font></p>
<p align="center"><img src="https://iili.io/JzsmqzJ.png"/>
<p align="center"><font size="5"> <b>Example without LoRA (min_p, chatML)</b></font></p>
<p align="center"><img src="https://iili.io/JzsmCsR.png"/>

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2198        | 0.63  | 30   | 0.9055          |
| 1.1206        | 1.26  | 60   | 0.8951          |
| 1.1319        | 1.89  | 90   | 0.8904          |
| 1.0031        | 2.51  | 120  | 0.9174          |


### Framework versions

- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0