Instructions to use ravi-huggingface/test_dir with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ravi-huggingface/test_dir with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base") model = PeftModel.from_pretrained(base_model, "ravi-huggingface/test_dir") - Transformers
How to use ravi-huggingface/test_dir with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ravi-huggingface/test_dir", dtype="auto") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: peft
license: apache-2.0
base_model: google/flan-t5-base
tags:
- base_model:adapter:google/flan-t5-base
- lora
- transformers
model-index:
- name: test_dir
results: []
test_dir
This model is a fine-tuned version of google/flan-t5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2442
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: 5e-05
- train_batch_size: 1
- 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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.4333 | 1.0 | 50 | 0.2442 |
Framework versions
- PEFT 0.18.0
- Transformers 4.57.1
- Pytorch 2.9.1+cpu
- Datasets 4.4.1
- Tokenizers 0.22.1