---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
tags:
- axolotl
- generated_from_trainer
datasets:
- cyberbabooshka/MNLP_M2_mcqa_dataset
model-index:
- name: base_noreasoning
  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: `0.10.0.dev0`
```yaml
base_model: Qwen/Qwen3-0.6B-Base
hub_model_id: cyberbabooshka/base_noreasoning
wandb_name: base

tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false

num_processes: 64
dataset_processes: 64
dataset_prepared_path: last_run_prepared

chat_template: jinja
chat_template_jinja: >-
  {%- for message in messages %}
    {{- '<|im_start|>' + message.role + '\n' + message.content.lstrip('\n') + '<|im_end|>' + '\n' }}
  {%- endfor %}
  {%- if add_generation_prompt %}
    {{- '<|im_start|>assistant\n' }}
  {%- endif %}

datasets:
  - path: cyberbabooshka/MNLP_M2_mcqa_dataset
    split: train
    type: chat_template
    field_messages: messages
    train_on_eos: turn
    train_on_eot: turn
    message_property_mappings:
      role: role
      content: content
    roles:
      user:
        - user
      assistant:
        - assistant

test_datasets:
  - path: cyberbabooshka/MNLP_M2_mcqa_dataset
    split: test
    type: chat_template
    field_messages: messages
    train_on_eos: turn
    train_on_eot: turn
    message_property_mappings:
      role: role
      content: content
    roles:
      user:
        - user
      assistant:
        - assistant

output_dir: ./outputs

sequence_len: 2048
batch_flattening: true
sample_packing: false

wandb_project: mnlp
wandb_entity: aleksandr-dremov-epfl
wandb_watch:
wandb_log_model:

gradient_accumulation_steps: 1
eval_batch_size: 16
micro_batch_size: 12

optimizer: ademamix_8bit
weight_decay: 0.01

learning_rate: 0.00001
warmup_steps: 500

wsd_final_lr_factor: 0.0
wsd_init_div_factor: 100
wsd_fract_decay: 0.2
wsd_decay_type: "sqrt"
wsd_sqrt_power: 0.5
wsd_cooldown_start_lr_factor: 1.0

bf16: auto
tf32: false

torch_compile: true
flash_attention: true
gradient_checkpointing: false

resume_from_checkpoint:
auto_resume_from_checkpoints: true

logging_steps: 16
eval_steps: 2000
save_steps: 1000
max_steps: 35000
num_epochs: 20000000
save_total_limit: 2

special_tokens:
  eos_token: "<|im_end|>"
  pad_token: "<|endoftext|>"

eot_tokens:
  - <|im_end|>

plugins:
  - axolotl_wsd.WSDSchedulerPlugin

```

</details><br>

# base_noreasoning

This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on the cyberbabooshka/MNLP_M2_mcqa_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7964

## 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: 12
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 24
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADEMAMIX_8BIT and the args are:
No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- training_steps: 35000

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| No log        | 0.0000 | 1     | 0.9810          |
| 0.8508        | 0.0556 | 2000  | 0.8516          |
| 0.8877        | 0.1111 | 4000  | 0.8365          |
| 0.8851        | 0.1667 | 6000  | 0.8281          |
| 0.8193        | 0.2223 | 8000  | 0.8222          |
| 0.8298        | 0.2778 | 10000 | 0.8177          |
| 0.8439        | 0.3334 | 12000 | 0.8141          |
| 0.8364        | 0.3890 | 14000 | 0.8111          |
| 0.8015        | 0.4445 | 16000 | 0.8085          |
| 0.8112        | 0.5001 | 18000 | 0.8062          |
| 0.7972        | 0.5556 | 20000 | 0.8042          |
| 0.8264        | 0.6112 | 22000 | 0.8024          |
| 0.7728        | 0.6668 | 24000 | 0.8008          |
| 0.7762        | 0.7223 | 26000 | 0.7992          |
| 0.8185        | 0.7779 | 28000 | 0.7978          |
| 0.8235        | 0.8335 | 30000 | 0.7967          |
| 0.7812        | 0.8890 | 32000 | 0.7964          |
| 0.7872        | 0.9446 | 34000 | 0.7964          |


### Framework versions

- Transformers 4.52.1
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1