Instructions to use caffeic/lora-flan-5-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use caffeic/lora-flan-5-small with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small") model = PeftModel.from_pretrained(base_model, "caffeic/lora-flan-5-small") - Transformers
How to use caffeic/lora-flan-5-small with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("caffeic/lora-flan-5-small", dtype="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: peft
license: apache-2.0
base_model: google/flan-t5-small
tags:
- base_model:adapter:google/flan-t5-small
- lora
- transformers
model-index:
- name: lora-flan-5-small
results: []
lora-flan-5-small
This model is a fine-tuned version of google/flan-t5-small on the None 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: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
Training results
Framework versions
- PEFT 0.18.1
- Transformers 5.5.4
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2