---
library_name: peft
base_model: AnnaelleMyriam/SFT_M3_model
tags:
- mcqa
- question-answering
- sft
- lora
- qwen
- unsloth
- generated_from_trainer
model-index:
- name: MNLP_M3_mcqa_sft_model
  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. -->

# MNLP_M3_mcqa_sft_model

This model is a fine-tuned version of [AnnaelleMyriam/SFT_M3_model](https://huggingface.co/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