---
library_name: transformers
base_model: minpeter/pretrained-tiny-ko
tags:
- axolotl
- generated_from_trainer
datasets:
- lemon-mint/Korean-FineTome-100k
- lemon-mint/smol-koreantalk
model-index:
- name: ko-tiny-exp
  results: []
---

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should probably proofread and complete it, then remove this comment. -->

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<details><summary>See axolotl config</summary>

axolotl version: `0.10.0.dev0`
```yaml
base_model: minpeter/pretrained-tiny-ko

chat_template: chatml
datasets:
  - path: lemon-mint/Korean-FineTome-100k
    type: chat_template
    split: train[:20%]
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
  - path: lemon-mint/smol-koreantalk
    type: chat_template
    split: train[:20%]
    field_messages: messages
    message_property_mappings:
      role: role
      content: content
dataset_prepared_path: last_run_prepared
val_set_size: 0.05

hub_model_id: minpeter/ko-tiny-exp
output_dir: ./ouputs/ko-tiny-exp
wandb_project: "axolotl"
wandb_entity: "kasfiekfs-e"

save_steps: 200
warmup_steps: 100
eval_steps: 200

sequence_len: 1024
sample_packing: true
pad_to_sequence_len: true

gradient_accumulation_steps: 4
micro_batch_size: 32

optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

bf16: auto
tf32: false

added_tokens_overrides:
  128001: "<|im_end|>"
  128002: "<|im_start|>"

special_tokens:
  bos_token: <|begin_of_text|>
  eos_token: <|im_end|>
  pad_token: <|im_end|>

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

num_epochs: 2
weight_decay: 0.0

```

</details><br>

# ko-tiny-exp

This model is a fine-tuned version of [minpeter/pretrained-tiny-ko](https://huggingface.co/minpeter/pretrained-tiny-ko) on the lemon-mint/Korean-FineTome-100k and the lemon-mint/smol-koreantalk datasets.
It achieves the following results on the evaluation set:
- Loss: 3.6038

## 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: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- total_eval_batch_size: 128
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT 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: 100
- training_steps: 102

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.5674        | 0.0193 | 1    | 3.6038          |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1