Instructions to use MarZaf/fine_tuned_student_kd_rag_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MarZaf/fine_tuned_student_kd_rag_3 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("MarZaf/fine_tuned_student_kd_rag_3") model = AutoModelForSeq2SeqLM.from_pretrained("MarZaf/fine_tuned_student_kd_rag_3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
fine_tuned_student_kd_rag_3
This model is a fine-tuned version of MarZaf/fine_tuned_student_kd_transformer-augmented4.1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0936
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use paged_adamw_32bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.1775 | 1.0 | 5072 | 0.0731 |
| 0.1368 | 2.0 | 10144 | 0.0794 |
| 0.116 | 3.0 | 15216 | 0.0831 |
| 0.0951 | 4.0 | 20288 | 0.0866 |
| 0.0859 | 5.0 | 25360 | 0.0902 |
| 0.0735 | 6.0 | 30432 | 0.0936 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.2
- Downloads last month
- 8
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support