---
library_name: transformers
license: apache-2.0
base_model: EleutherAI/pythia-160m
tags:
- generated_from_trainer
model-index:
- name: pythia_160m_sft
  results: []
datasets:
- tatsu-lab/alpaca_farm
---

<!-- 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. -->

# pythia_160m_sft

This model is a fine-tuned version of [EleutherAI/pythia-160m](https://huggingface.co/EleutherAI/pythia-160m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1033

## 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.3332        | 0.0889 | 100  | 2.2776          |
| 2.3071        | 0.1778 | 200  | 2.2564          |
| 2.2941        | 0.2667 | 300  | 2.2301          |
| 2.2548        | 0.3556 | 400  | 2.2094          |
| 2.2246        | 0.4444 | 500  | 2.1973          |
| 2.2189        | 0.5333 | 600  | 2.1705          |
| 2.181         | 0.6222 | 700  | 2.1498          |
| 2.1515        | 0.7111 | 800  | 2.1399          |
| 2.1333        | 0.8    | 900  | 2.1212          |
| 2.1139        | 0.8889 | 1000 | 2.1050          |
| 2.0778        | 0.9778 | 1100 | 2.0970          |
| 1.8312        | 1.0667 | 1200 | 2.1071          |
| 1.7405        | 1.1556 | 1300 | 2.1022          |
| 1.7284        | 1.2444 | 1400 | 2.1049          |
| 1.7554        | 1.3333 | 1500 | 2.1023          |
| 1.732         | 1.4222 | 1600 | 2.0934          |
| 1.7474        | 1.5111 | 1700 | 2.0917          |
| 1.7495        | 1.6    | 1800 | 2.0820          |
| 1.7449        | 1.6889 | 1900 | 2.0770          |
| 1.7474        | 1.7778 | 2000 | 2.0708          |
| 1.7447        | 1.8667 | 2100 | 2.0643          |
| 1.7138        | 1.9556 | 2200 | 2.0550          |
| 1.5662        | 2.0444 | 2300 | 2.1122          |
| 1.4506        | 2.1333 | 2400 | 2.1203          |
| 1.4282        | 2.2222 | 2500 | 2.1225          |
| 1.4302        | 2.3111 | 2600 | 2.1173          |
| 1.4471        | 2.4    | 2700 | 2.1156          |
| 1.4217        | 2.4889 | 2800 | 2.1168          |
| 1.428         | 2.5778 | 2900 | 2.1126          |
| 1.4206        | 2.6667 | 3000 | 2.1059          |
| 1.4315        | 2.7556 | 3100 | 2.1068          |
| 1.4345        | 2.8444 | 3200 | 2.1037          |
| 1.4034        | 2.9333 | 3300 | 2.1033          |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3