---
library_name: peft
base_model: TitanML/tiny-mixtral
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 4de6d45f-421b-462f-8f05-b63f7c8fdea1
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

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

# 4de6d45f-421b-462f-8f05-b63f7c8fdea1

This model is a fine-tuned version of [TitanML/tiny-mixtral](https://huggingface.co/TitanML/tiny-mixtral) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 8.3056

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.000217
- train_batch_size: 4
- eval_batch_size: 4
- seed: 170
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 392

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0026 | 1    | 10.5671         |
| 8.9191        | 0.1277 | 50   | 8.9119          |
| 8.654         | 0.2554 | 100  | 8.7470          |
| 8.3712        | 0.3831 | 150  | 8.5024          |
| 8.3764        | 0.5109 | 200  | 8.4021          |
| 8.3127        | 0.6386 | 250  | 8.3437          |
| 8.2662        | 0.7663 | 300  | 8.3173          |
| 8.2846        | 0.8940 | 350  | 8.3056          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1