Downloads · 30 days
7
29% of all-time downloads
MNLP_M3_mcqa_anti_overfit
aymanbakiri/MNLP_M3_mcqa_anti_overfit
MNLP_M3_mcqa_anti_overfit is a machine learning model from aymanbakiri. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft.
should probably proofread and complete it, then remove this comment. --
All-time downloads
24
Public
Repo size
336 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors973 MB · 95%
At a glance
- Library
- peft
- Access
- Public
- Created
- Jun 10, 2025
- Updated
- Jun 10, 2025
- SHA
- 0a7b896c
Base models
Model card
From the Hugging Face model README
<!-- 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. -->
MNLP_M3_mcqa_anti_overfit
This model is a fine-tuned version of AnnaelleMyriam/SFT_M3_model on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.9834
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: linear
- lr_scheduler_warmup_ratio: 0.15
- num_epochs: 2
- label_smoothing_factor: 0.1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.7902 | 0.05 | 50 | 3.8886 |
| 3.8278 | 0.1 | 100 | 3.7602 |
| 3.52 | 0.15 | 150 | 3.3903 |
| 3.0491 | 0.2 | 200 | 2.9518 |
| 2.7477 | 0.25 | 250 | 2.7795 |
| 2.6077 | 0.3 | 300 | 2.6335 |
| 2.4566 | 0.35 | 350 | 2.4626 |
| 2.3592 | 0.4 | 400 | 2.3554 |
| 2.3056 | 0.45 | 450 | 2.2539 |
| 2.1646 | 0.5 | 500 | 2.1980 |
| 2.0673 | 0.55 | 550 | 2.1717 |
| 2.029 | 0.6 | 600 | 2.1452 |
| 2.0557 | 0.65 | 650 | 2.1068 |
| 2.0771 | 0.7 | 700 | 2.0875 |
| 2.0488 | 0.75 | 750 | 2.0660 |
| 2.0679 | 0.8 | 800 | 2.0526 |
| 2.063 | 0.85 | 850 | 2.0389 |
| 2.0567 | 0.9 | 900 | 2.0321 |
| 2.0714 | 0.95 | 950 | 2.0214 |
| 1.9788 | 1.0 | 1000 | 2.0160 |
| 1.9398 | 1.05 | 1050 | 2.0144 |
| 1.928 | 1.1 | 1100 | 2.0196 |
| 1.9747 | 1.15 | 1150 | 2.0117 |
| 2.0118 | 1.2 | 1200 | 1.9938 |
| 1.9771 | 1.25 | 1250 | 1.9881 |
| 1.9078 | 1.3 | 1300 | 1.9844 |
| 1.9312 | 1.35 | 1350 | 1.9838 |
| 2.0094 | 1.4 | 1400 | 1.9834 |
Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
- Library
- peft
- Created
- Jun 10, 2025
- Updated
- Jun 10, 2025