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MNLP_M3_mcqa_sft_model
aymanbakiri/MNLP_M3_mcqa_sft_model
MNLP_M3_mcqa_sft_model is a question answering model from aymanbakiri. Use it when the input is a question plus a passage. It is set up for peft.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Question Answering
- Library
- peft
- Access
- Public
- Created
- Jun 10, 2025
- Updated
- Jun 10, 2025
- SHA
- 7d771d1a
Try a prompt
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_sft_model
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: 0.5993
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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: 0.05
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3535 | 0.1352 | 250 | 0.4926 |
| 0.4864 | 0.2703 | 500 | 0.3696 |
| 0.342 | 0.4055 | 750 | 0.3518 |
| 0.3763 | 0.5407 | 1000 | 0.3259 |
| 0.3566 | 0.6759 | 1250 | 0.3335 |
| 0.2901 | 0.8110 | 1500 | 0.3195 |
| 0.3235 | 0.9462 | 1750 | 0.3060 |
| 0.2315 | 1.0811 | 2000 | 0.3930 |
| 0.2842 | 1.2163 | 2250 | 0.3920 |
| 0.2183 | 1.3514 | 2500 | 0.3796 |
| 0.1824 | 1.4866 | 2750 | 0.3979 |
| 0.1877 | 1.6218 | 3000 | 0.4335 |
| 0.1821 | 1.7570 | 3250 | 0.3981 |
| 0.2364 | 1.8921 | 3500 | 0.3922 |
| 0.1339 | 2.0270 | 3750 | 0.4119 |
| 0.1073 | 2.1622 | 4000 | 0.5467 |
| 0.0722 | 2.2974 | 4250 | 0.5596 |
| 0.113 | 2.4325 | 4500 | 0.5158 |
| 0.1467 | 2.5677 | 4750 | 0.4852 |
| 0.1675 | 2.7029 | 5000 | 0.5103 |
| 0.101 | 2.8381 | 5250 | 0.5661 |
| 0.1935 | 2.9732 | 5500 | 0.4946 |
| 0.1069 | 3.1081 | 5750 | 0.5844 |
| 0.0799 | 3.2433 | 6000 | 0.5681 |
| 0.0803 | 3.3785 | 6250 | 0.5795 |
| 0.0744 | 3.5137 | 6500 | 0.5935 |
| 0.0464 | 3.6488 | 6750 | 0.6010 |
| 0.0643 | 3.7840 | 7000 | 0.6009 |
| 0.0871 | 3.9192 | 7250 | 0.5993 |
Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
- Task
- Question Answering
- Library
- peft
- Created
- Jun 10, 2025
- Updated
- Jun 10, 2025