---
library_name: transformers
license: apache-2.0
base_model: cyberbabooshka/base_noreasoning
tags:
- axolotl
- generated_from_trainer
datasets:
- cyberbabooshka/MNLP_M2_mcqa_dataset
model-index:
- name: MNLP_M2_mcqa_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. -->

[<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: cyberbabooshka/base_noreasoning
hub_model_id: cyberbabooshka/MNLP_M2_mcqa_model
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 %}
    {{- message.content.strip('\n') + '\n' }}
  {%- endfor %}
  {%- if not add_generation_prompt %}
    {{- '<|im_end|>' }}
  {%- endif %}


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

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

output_dir: ./outputs_mcqa

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: 100

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: 500
save_steps: 500
max_steps: 1000000
num_epochs: 1
save_total_limit: 2

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

eot_tokens:
  - <|im_end|>

plugins:
  - axolotl_wsd.WSDSchedulerPlugin

```

</details><br>

# MNLP_M2_mcqa_model

This model is a fine-tuned version of [cyberbabooshka/base_noreasoning](https://huggingface.co/cyberbabooshka/base_noreasoning) on the cyberbabooshka/MNLP_M2_mcqa_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6772

## 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: 100
- training_steps: 8438

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0001 | 1    | 2.2371          |
| 0.8956        | 0.0593 | 500  | 0.7674          |
| 0.9093        | 0.1185 | 1000 | 0.7335          |
| 0.8544        | 0.1778 | 1500 | 0.7159          |
| 0.8503        | 0.2370 | 2000 | 0.7074          |
| 0.8781        | 0.2963 | 2500 | 0.7016          |
| 0.8171        | 0.3555 | 3000 | 0.6968          |
| 0.9179        | 0.4148 | 3500 | 0.6930          |
| 0.845         | 0.4740 | 4000 | 0.6895          |
| 0.8885        | 0.5333 | 4500 | 0.6865          |
| 0.9432        | 0.5926 | 5000 | 0.6844          |
| 0.7451        | 0.6518 | 5500 | 0.6825          |
| 0.8675        | 0.7111 | 6000 | 0.6811          |
| 0.8606        | 0.7703 | 6500 | 0.6793          |
| 0.8602        | 0.8000 | 6750 | 0.6793          |
| 0.8458        | 0.8296 | 7000 | 0.6778          |
| 0.9051        | 0.8888 | 7500 | 0.6772          |
| 0.8589        | 0.9481 | 8000 | 0.6772          |


### Framework versions

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