Instructions to use fragnarx/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fragnarx/output with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/flan-t5-small") model = PeftModel.from_pretrained(base_model, "fragnarx/output") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: peft | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| base_model: google/flan-t5-small | |
| datasets: | |
| - generator | |
| model-index: | |
| - name: output | |
| 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. --> | |
| # output | |
| This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the generator dataset. | |
| ## 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: 0.0002 | |
| - train_batch_size: 4 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 1 | |
| ### Framework versions | |
| - PEFT 0.10.1.dev0 | |
| - Transformers 4.39.1 | |
| - Pytorch 2.2.1+cpu | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.2 |