Instructions to use alizaidi/lora-mt5-goud with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use alizaidi/lora-mt5-goud with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/mt5-small") model = PeftModel.from_pretrained(base_model, "alizaidi/lora-mt5-goud") - Notebooks
- Google Colab
- Kaggle
lora-mt5-goud
This model is a fine-tuned version of google/mt5-small on an unknown 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: 0.001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
Training results
Framework versions
- PEFT 0.11.1
- Transformers 4.43.1
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Downloads last month
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Model tree for alizaidi/lora-mt5-goud
Base model
google/mt5-small