2.8 KiB
2.8 KiB
library_name, license, base_model, tags, model-index
| library_name | license | base_model | tags | model-index | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| transformers | llama3.2 | meta-llama/Llama-3.2-1B-Instruct |
|
|
train_boolq_42_1776331558
This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on the boolq dataset. It achieves the following results on the evaluation set:
- Loss: 0.1885
- Num Input Tokens Seen: 12333600
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-06
- train_batch_size: 8
- eval_batch_size: 8
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|---|---|---|---|---|
| 0.2277 | 0.2507 | 266 | 0.2505 | 618432 |
| 0.2193 | 0.5014 | 532 | 0.3166 | 1225408 |
| 0.2554 | 0.7521 | 798 | 0.2179 | 1851072 |
| 0.3676 | 1.0028 | 1064 | 0.1885 | 2475808 |
| 0.165 | 1.2535 | 1330 | 0.4608 | 3091552 |
| 0.2207 | 1.5042 | 1596 | 0.3545 | 3699104 |
| 0.1138 | 1.7549 | 1862 | 0.3500 | 4324256 |
| 0.0762 | 2.0057 | 2128 | 0.3345 | 4940992 |
| 0.0898 | 2.2564 | 2394 | 0.4647 | 5558144 |
| 0.0692 | 2.5071 | 2660 | 0.4098 | 6183872 |
| 0.227 | 2.7578 | 2926 | 0.4303 | 6806208 |
| 0.0004 | 3.0085 | 3192 | 0.3937 | 7421856 |
| 0.0 | 3.2592 | 3458 | 0.5191 | 8043744 |
| 0.0002 | 3.5099 | 3724 | 0.4636 | 8660768 |
| 0.0 | 3.7606 | 3990 | 0.5201 | 9286304 |
| 0.0001 | 4.0113 | 4256 | 0.5146 | 9894624 |
| 0.0782 | 4.2620 | 4522 | 0.5548 | 10512416 |
| 0.0 | 4.5127 | 4788 | 0.5418 | 11115040 |
| 0.0 | 4.7634 | 5054 | 0.5422 | 11736672 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.21.4