---
license: llama3
base_model: maywell/Llama-3-Ko-Luxia-Instruct
tags:
- generated_from_trainer
model-index:
- name: data/output/1min-v2-luxia-8b
  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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
base_model: maywell/Llama-3-Ko-Luxia-Instruct
trust_remote_code: true
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
  - path: "../data/generated_ds.json"
    type: alpaca
    conversation: chatml
dataset_prepared_path: ../data/dataset_v2_pre
val_set_size: 0.05
output_dir: ../data/output/1min-v2-luxia-8b
sequence_len: 1024
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
wandb_project: 
wandb_entity: 
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 10
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 2e-6
train_on_inputs: false
group_by_length: false
bf16: auto
fp16: null
tf32: false
gradient_checkpointing: true
early_stopping_patience: null
resume_from_checkpoint: null
local_rank: null
logging_steps: 1
xformers_attention: null
flash_attention: true
warmup_steps: 10
evals_per_epoch: 4
eval_table_size: null
eval_max_new_tokens: 128
saves_per_epoch: 1
save_total_limit: 4
debug: true
deepspeed: deepspeed_configs/zero2.json
weight_decay: 0.0
special_tokens:
  pad_token: <|end_of_text|>
```

</details><br>

# data/output/1min-v2-luxia-8b

This model is a fine-tuned version of [maywell/Llama-3-Ko-Luxia-Instruct](https://huggingface.co/maywell/Llama-3-Ko-Luxia-Instruct) on the manipulated [instructkr/ko_youtube_transcription_v2_filtered](https://huggingface.co/datasets/instructkr/ko_youtube_transcription_v2_filtered) dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0986

## 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-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 28
- total_eval_batch_size: 7
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.6145        | 0.0513 | 1    | 2.7217          |
| 2.7668        | 0.2564 | 5    | 2.7018          |
| 2.6304        | 0.5128 | 10   | 2.5065          |
| 2.3635        | 0.7692 | 15   | 2.3580          |
| 2.4553        | 1.0256 | 20   | 2.2813          |
| 2.2344        | 1.2436 | 25   | 2.2339          |
| 2.4562        | 1.5    | 30   | 2.2017          |
| 2.0943        | 1.7564 | 35   | 2.1726          |
| 2.0695        | 2.0128 | 40   | 2.1425          |
| 1.8616        | 2.2308 | 45   | 2.1171          |
| 2.0498        | 2.4872 | 50   | 2.1040          |
| 1.9028        | 2.7436 | 55   | 2.0984          |
| 1.9057        | 3.0    | 60   | 2.0841          |
| 1.7464        | 3.2179 | 65   | 2.0784          |
| 1.8284        | 3.4744 | 70   | 2.0788          |
| 1.8866        | 3.7308 | 75   | 2.0761          |
| 1.8927        | 3.9872 | 80   | 2.0673          |
| 1.5778        | 4.2051 | 85   | 2.0779          |
| 1.7274        | 4.4615 | 90   | 2.0934          |
| 1.7431        | 4.7179 | 95   | 2.0652          |
| 1.8728        | 4.9744 | 100  | 2.0618          |
| 1.5729        | 5.1923 | 105  | 2.0837          |
| 1.4631        | 5.4487 | 110  | 2.0873          |
| 1.4758        | 5.7051 | 115  | 2.0744          |
| 1.5289        | 5.9615 | 120  | 2.0899          |
| 1.515         | 6.1795 | 125  | 2.0919          |
| 1.5757        | 6.4359 | 130  | 2.0978          |
| 1.5392        | 6.6923 | 135  | 2.0986          |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1