|
--- |
|
license: apache-2.0 |
|
library_name: peft |
|
tags: |
|
- trl |
|
- sft |
|
- generated_from_trainer |
|
datasets: |
|
- generator |
|
base_model: rishiraj/CatPPT-base |
|
model-index: |
|
- name: catppt_2 |
|
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. --> |
|
|
|
# catppt_2 |
|
|
|
This model is a fine-tuned version of [rishiraj/CatPPT-base](https://huggingface.co/rishiraj/CatPPT-base) on the generator dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 0.3328 |
|
|
|
## 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: 0.0001 |
|
- train_batch_size: 4 |
|
- eval_batch_size: 2 |
|
- seed: 42 |
|
- gradient_accumulation_steps: 32 |
|
- total_train_batch_size: 128 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_ratio: 0.03 |
|
- training_steps: 130 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:-----:|:----:|:---------------:| |
|
| 1.7962 | 0.97 | 10 | 1.1737 | |
|
| 1.0723 | 1.94 | 20 | 0.9357 | |
|
| 0.8491 | 2.91 | 30 | 0.7148 | |
|
| 0.6214 | 3.88 | 40 | 0.5303 | |
|
| 0.4837 | 4.85 | 50 | 0.4475 | |
|
| 0.4223 | 5.82 | 60 | 0.4044 | |
|
| 0.3822 | 6.79 | 70 | 0.3762 | |
|
| 0.3575 | 7.76 | 80 | 0.3568 | |
|
| 0.3477 | 8.73 | 90 | 0.3449 | |
|
| 0.3283 | 9.7 | 100 | 0.3368 | |
|
| 0.3254 | 10.67 | 110 | 0.3337 | |
|
| 0.3177 | 11.64 | 120 | 0.3328 | |
|
| 0.3299 | 12.61 | 130 | 0.3328 | |
|
|
|
|
|
### Framework versions |
|
|
|
- PEFT 0.7.2.dev0 |
|
- Transformers 4.38.1 |
|
- Pytorch 2.1.2+cu121 |
|
- Datasets 2.16.1 |
|
- Tokenizers 0.15.0 |
