---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
tags:
- generated_from_trainer
datasets:
- timarni/MNLP_M3_mcqa_dataset
model-index:
- name: outputs/base_it_hard
  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: Qwen/Qwen3-0.6B-Base
# 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
    name: stem_instruction_tuning_hard
    type: alpaca
    split: train

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

sequence_len: 2048 # 4096
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true

# 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: base_it_hard
wandb_log_model:

gradient_accumulation_steps: 4 # 2
micro_batch_size: 2 # 1
num_epochs: 5
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00001 # 0.00005
cosine_min_lr_ratio: 0.1

bf16: auto
tf32: true

gradient_checkpointing: offload
gradient_checkpointing_kwargs:
  use_reentrant: false
resume_from_checkpoint:
logging_steps: 1
flash_attention: true

warmup_ratio: 0.05
evals_per_epoch: 4
saves_per_epoch: 2
save_total_limit: 10
weight_decay: 0.01
special_tokens:

```

</details><br>

# outputs/base_it_hard

This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on the timarni/MNLP_M3_mcqa_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 4.5354

## 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: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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: 45
- num_epochs: 5.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.8271        | 0.0055 | 1    | 6.2702          |
| 0.1398        | 0.2490 | 45   | 4.7948          |
| 0.1439        | 0.4979 | 90   | 4.3628          |
| 0.1377        | 0.7469 | 135  | 4.2137          |
| 0.1436        | 0.9959 | 180  | 4.2396          |
| 0.1086        | 1.2434 | 225  | 4.2662          |
| 0.1018        | 1.4924 | 270  | 4.3334          |
| 0.1226        | 1.7414 | 315  | 4.3240          |
| 0.13          | 1.9903 | 360  | 4.3957          |
| 0.1269        | 2.2379 | 405  | 4.3869          |
| 0.11          | 2.4869 | 450  | 4.4244          |
| 0.1081        | 2.7358 | 495  | 4.4782          |
| 0.1139        | 2.9848 | 540  | 4.5098          |
| 0.1041        | 3.2324 | 585  | 4.4869          |
| 0.1052        | 3.4813 | 630  | 4.5032          |
| 0.1143        | 3.7303 | 675  | 4.5032          |
| 0.1144        | 3.9793 | 720  | 4.5265          |
| 0.104         | 4.2268 | 765  | 4.5161          |
| 0.1343        | 4.4758 | 810  | 4.5280          |
| 0.1217        | 4.7248 | 855  | 4.5158          |
| 0.1158        | 4.9737 | 900  | 4.5354          |


### Framework versions

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