---
library_name: transformers
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: cadgpt-gpt2-train
  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. -->

# cadgpt-gpt2-train

This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0298

## 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.2987        | 0.0544 | 200  | 0.1816          |
| 0.1236        | 0.1088 | 400  | 0.0997          |
| 0.0876        | 0.1633 | 600  | 0.0692          |
| 0.0703        | 0.2177 | 800  | 0.0554          |
| 0.0601        | 0.2721 | 1000 | 0.0531          |
| 0.0553        | 0.3265 | 1200 | 0.0453          |
| 0.0514        | 0.3810 | 1400 | 0.0427          |
| 0.0473        | 0.4354 | 1600 | 0.0403          |
| 0.0463        | 0.4898 | 1800 | 0.0404          |
| 0.0461        | 0.5442 | 2000 | 0.0386          |
| 0.0428        | 0.5986 | 2200 | 0.0376          |
| 0.0428        | 0.6531 | 2400 | 0.0367          |
| 0.0395        | 0.7075 | 2600 | 0.0353          |
| 0.0396        | 0.7619 | 2800 | 0.0351          |
| 0.0388        | 0.8163 | 3000 | 0.0347          |
| 0.0383        | 0.8707 | 3200 | 0.0350          |
| 0.038         | 0.9252 | 3400 | 0.0359          |
| 0.0371        | 0.9796 | 3600 | 0.0343          |
| 0.0364        | 1.0340 | 3800 | 0.0328          |
| 0.0372        | 1.0884 | 4000 | 0.0331          |
| 0.0363        | 1.1429 | 4200 | 0.0324          |
| 0.0351        | 1.1973 | 4400 | 0.0334          |
| 0.0347        | 1.2517 | 4600 | 0.0317          |
| 0.0342        | 1.3061 | 4800 | 0.0315          |
| 0.0344        | 1.3605 | 5000 | 0.0314          |
| 0.0337        | 1.4150 | 5200 | 0.0309          |
| 0.0338        | 1.4694 | 5400 | 0.0310          |
| 0.0334        | 1.5238 | 5600 | 0.0308          |
| 0.0341        | 1.5782 | 5800 | 0.0306          |
| 0.033         | 1.6327 | 6000 | 0.0319          |
| 0.0335        | 1.6871 | 6200 | 0.0304          |
| 0.0322        | 1.7415 | 6400 | 0.0302          |
| 0.0329        | 1.7959 | 6600 | 0.0301          |
| 0.0336        | 1.8503 | 6800 | 0.0300          |
| 0.0328        | 1.9048 | 7000 | 0.0300          |
| 0.0331        | 1.9592 | 7200 | 0.0298          |


### Framework versions

- Transformers 4.56.1
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.0