---
library_name: peft
license: apache-2.0
base_model: EleutherAI/pythia-410m-deduped
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 462b79a5-6086-4314-b090-029009c90b2d
  results: []
---

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[<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>

# 462b79a5-6086-4314-b090-029009c90b2d

This model is a fine-tuned version of [EleutherAI/pythia-410m-deduped](https://huggingface.co/EleutherAI/pythia-410m-deduped) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8018

## 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: 0.000202
- train_batch_size: 4
- eval_batch_size: 4
- seed: 20
- 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: 500

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0005 | 1    | 4.2533          |
| 3.1402        | 0.0239 | 50   | 1.5783          |
| 2.985         | 0.0478 | 100  | 1.1787          |
| 2.6554        | 0.0717 | 150  | 1.1248          |
| 3.5819        | 0.0956 | 200  | 1.1990          |
| 2.4072        | 0.1194 | 250  | 1.1132          |
| 2.1323        | 0.1433 | 300  | 0.9466          |
| 2.3506        | 0.1672 | 350  | 0.8946          |
| 2.0516        | 0.1911 | 400  | 0.8264          |
| 1.9909        | 0.2150 | 450  | 0.8030          |
| 2.0245        | 0.2389 | 500  | 0.8018          |


### Framework versions

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