Instructions to use RiverTest/content with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use RiverTest/content with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("RiverTest/RiverMTG13") model = PeftModel.from_pretrained(base_model, "RiverTest/content") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| datasets: | |
| - generator | |
| base_model: RiverTest/RiverMTG13 | |
| model-index: | |
| - name: RiverTest/RiverMTG13 | |
| 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. --> | |
| # RiverTest/RiverMTG13 | |
| This model is a fine-tuned version of [RiverTest/RiverMTG13](https://huggingface.co/RiverTest/RiverMTG13) on the generator dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.1894 | |
| ## 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: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - num_epochs: 3 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.3271 | 1.0 | 42 | 1.0205 | | |
| | 0.2385 | 2.0 | 84 | 1.1050 | | |
| | 0.0392 | 3.0 | 126 | 1.1894 | | |
| ### Framework versions | |
| - PEFT 0.7.1 | |
| - Transformers 4.36.2 | |
| - Pytorch 2.1.2+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.0 |