Transformers
TensorBoard
Safetensors
t5
text2text-generation
Generated from Trainer
text-generation-inference
Instructions to use RayenLLM/Bad_To_Good with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RayenLLM/Bad_To_Good with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("RayenLLM/Bad_To_Good") model = AutoModelForSeq2SeqLM.from_pretrained("RayenLLM/Bad_To_Good", device_map="auto") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from RayenLLM/Bad_To_Good: direct link, hf CLI and curl.
- Browser
- Download file 1.32 kB
-
https://huggingface.co/RayenLLM/Bad_To_Good/resolve/main/README.md
- Command line
-
hf download hf://RayenLLM/Bad_To_Good/README.md
-
curl -L -o README.md https://huggingface.co/RayenLLM/Bad_To_Good/resolve/main/README.md
1.32 kB
metadata
library_name: transformers
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: Bad_To_Good
results: []
Bad_To_Good
This model is a fine-tuned version of 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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 6 | 15.1201 |
Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1