---
license: apache-2.0
base_model: HuggingFaceTB/SmolLM-360M-Instruct
tags:
- generated_from_trainer
model-index:
- name: SomlLm-360M-Ko-Instruct
  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. -->

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

axolotl version: `0.4.1`
```yaml
base_model: HuggingFaceTB/SmolLM-360M-Instruct
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false


chat_template: chatml
datasets:
    - path: CarrotAI/ko-instruction-dataset
      type: alpaca
    - path: werty1248/sharegpt-tagengo-gpt4-ko
      type: sharegpt
    - path: changpt/ko-lima-vicuna
      type: sharegpt
    - path: davidkim205/kollm-converations
      type: sharegpt
    - path: CarrotAI/Amazing-Instructions
      type: alpaca

dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./SomlLm-360M-Ko-Instruct

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 6
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

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

warmup_steps: 100
evals_per_epoch: 1
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

```

</details><br>

# SomlLm-360M-Ko-Instruct

This model is a fine-tuned version of [HuggingFaceTB/SmolLM-360M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM-360M-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1516

## 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: 6
- eval_batch_size: 6
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 96
- total_eval_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1401        | 0.9993 | 1316 | 1.1516          |


### Framework versions

- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1