|
--- |
|
library_name: transformers |
|
license: llama3.2 |
|
base_model: meta-llama/Llama-3.2-1B |
|
tags: |
|
- trl |
|
- sft |
|
- generated_from_trainer |
|
model-index: |
|
- name: rationale_model_e3_save5000_f4 |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# rationale_model_e3_save5000_f4 |
|
|
|
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 1.9369 |
|
|
|
## 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: 1e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 3 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | |
|
|:-------------:|:------:|:-----:|:---------------:| |
|
| 1.7536 | 0.1907 | 1000 | 1.9369 | |
|
| 1.3797 | 0.3813 | 2000 | 2.0320 | |
|
| 1.0216 | 0.5720 | 3000 | 2.1529 | |
|
| 0.6624 | 0.7626 | 4000 | 2.3760 | |
|
| 0.3893 | 0.9533 | 5000 | 2.7429 | |
|
| 0.1995 | 1.1439 | 6000 | 2.9766 | |
|
| 0.1703 | 1.3346 | 7000 | 3.0843 | |
|
| 0.1489 | 1.5253 | 8000 | 3.1774 | |
|
| 0.1249 | 1.7159 | 9000 | 3.3298 | |
|
| 0.1168 | 1.9066 | 10000 | 3.4572 | |
|
| 0.0977 | 2.0972 | 11000 | 3.5885 | |
|
| 0.0951 | 2.2879 | 12000 | 3.6941 | |
|
| 0.092 | 2.4786 | 13000 | 3.7847 | |
|
| 0.0894 | 2.6692 | 14000 | 3.9039 | |
|
| 0.086 | 2.8599 | 15000 | 3.9903 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.45.0 |
|
- Pytorch 2.3.0 |
|
- Datasets 2.14.4 |
|
- Tokenizers 0.20.3 |
|
|
