---
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: 1.9831

## 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-06
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 2.2935        | 0.0889 | 100  | 2.1426          |
| 2.153         | 0.1778 | 200  | 2.0977          |
| 2.1432        | 0.2667 | 300  | 2.0771          |
| 2.1131        | 0.3556 | 400  | 2.0633          |
| 2.0885        | 0.4444 | 500  | 2.0510          |
| 2.0956        | 0.5333 | 600  | 2.0403          |
| 2.0647        | 0.6222 | 700  | 2.0354          |
| 2.0498        | 0.7111 | 800  | 2.0273          |
| 2.0317        | 0.8    | 900  | 2.0202          |
| 2.0226        | 0.8889 | 1000 | 2.0150          |
| 1.992         | 0.9778 | 1100 | 2.0114          |
| 1.9639        | 1.0667 | 1200 | 2.0088          |
| 1.9302        | 1.1556 | 1300 | 2.0051          |
| 1.9381        | 1.2444 | 1400 | 2.0028          |
| 1.9595        | 1.3333 | 1500 | 2.0009          |
| 1.9325        | 1.4222 | 1600 | 1.9998          |
| 1.9481        | 1.5111 | 1700 | 1.9981          |
| 1.9572        | 1.6    | 1800 | 1.9956          |
| 1.9456        | 1.6889 | 1900 | 1.9944          |
| 1.9565        | 1.7778 | 2000 | 1.9922          |
| 1.9507        | 1.8667 | 2100 | 1.9905          |
| 1.9247        | 1.9556 | 2200 | 1.9881          |
| 1.8998        | 2.0444 | 2300 | 1.9874          |
| 1.9102        | 2.1333 | 2400 | 1.9873          |
| 1.8842        | 2.2222 | 2500 | 1.9876          |
| 1.876         | 2.3111 | 2600 | 1.9863          |
| 1.9001        | 2.4    | 2700 | 1.9856          |
| 1.8725        | 2.4889 | 2800 | 1.9859          |
| 1.868         | 2.5778 | 2900 | 1.9845          |
| 1.8803        | 2.6667 | 3000 | 1.9844          |
| 1.9002        | 2.7556 | 3100 | 1.9838          |
| 1.8941        | 2.8444 | 3200 | 1.9839          |
| 1.8548        | 2.9333 | 3300 | 1.9831          |


### Framework versions

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