---
library_name: transformers
base_model: timarni/qwen3_dpo
tags:
- generated_from_trainer
datasets:
- timarni/MNLP_M3_mcqa_dataset
model-index:
- name: outputs/dpo_it_bal
  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. -->

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.9.2`
```yaml
base_model: timarni/qwen3_dpo
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name

plugins:
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
strict: false

chat_template: qwen3
datasets:
  - path: timarni/MNLP_M3_mcqa_dataset # timarni/MNLP_intstruction_tuning
    name: stem_instruction_tuning_balanced_mini
    type: alpaca
    split: train

shuffle_merged_datasets: true

val_set_size: 0.1
output_dir: ./outputs/dpo_it_bal
dataset_prepared_path: last_run_prepared

sequence_len: 2048 #2048
sample_packing: true # was true -> need to check if it actually learns on the samples or not (better understand te hyperparam and event. install axolotl to debug)
eval_sample_packing: true
pad_to_sequence_len: true
train_on_inputs: false # NEW
# group_by_length: false NEW?

# To be sure that no LORA is done
adapter: null
lora: false
merge_lora: false

wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_watch:
wandb_name: wiki_it_bal
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 1 # 2
num_epochs: 6
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 5e-6 # 0.00005
cosine_min_lr_ratio: 0.1

warmup_ratio: 0.05
weight_decay: 0.01

bf16: auto
tf32: true

gradient_checkpointing: offload
gradient_checkpointing_kwargs:
  use_reentrant: false
resume_from_checkpoint:
logging_steps: 1
gradient_clipping: 1.0 # or max_grad_norm?
flash_attention: true

evals_per_epoch: 4
saves_per_epoch: 2
save_total_limit: 25
special_tokens:

```

</details><br>

# outputs/dpo_it_bal

This model is a fine-tuned version of [timarni/qwen3_dpo](https://huggingface.co/timarni/qwen3_dpo) on the timarni/MNLP_M3_mcqa_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1734

## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- total_eval_batch_size: 4
- optimizer: Use 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_steps: 24
- num_epochs: 6.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0143        | 0.0122 | 1    | 1.8038          |
| 0.988         | 0.2561 | 21   | 0.9164          |
| 0.2107        | 0.5122 | 42   | 0.1900          |
| 0.1751        | 0.7683 | 63   | 0.1814          |
| 0.1691        | 1.0244 | 84   | 0.1785          |
| 0.1521        | 1.2805 | 105  | 0.1759          |
| 0.1458        | 1.5366 | 126  | 0.1759          |
| 0.1822        | 1.7927 | 147  | 0.1749          |
| 0.153         | 2.0488 | 168  | 0.1736          |
| 0.1603        | 2.3049 | 189  | 0.1739          |
| 0.1474        | 2.5610 | 210  | 0.1751          |
| 0.2087        | 2.8171 | 231  | 0.1738          |
| 0.1599        | 3.0732 | 252  | 0.1732          |
| 0.1411        | 3.3293 | 273  | 0.1734          |
| 0.2014        | 3.5854 | 294  | 0.1744          |
| 0.1507        | 3.8415 | 315  | 0.1735          |
| 0.1684        | 4.0976 | 336  | 0.1735          |
| 0.1547        | 4.3537 | 357  | 0.1731          |
| 0.1469        | 4.6098 | 378  | 0.1738          |
| 0.155         | 4.8659 | 399  | 0.1736          |
| 0.162         | 5.1220 | 420  | 0.1735          |
| 0.1274        | 5.3780 | 441  | 0.1732          |
| 0.1397        | 5.6341 | 462  | 0.1736          |
| 0.1333        | 5.8902 | 483  | 0.1734          |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.1
- Tokenizers 0.21.1