See axolotl config
axolotl version: 0.4.0
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/output_fix_real.json"
type: alpaca
conversation: chatml
dataset_prepared_path: ../data/1min-luxia-data-pre
val_set_size: 0.1
output_dir: ../data/output/1min-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: 8
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|>
data/output/1min-luxia-8b
This model is a fine-tuned version of maywell/Llama-3-Ko-Luxia-Instruct on the modified maywell/ko_youtube_transcription_sample dataset. It achieves the following results on the evaluation set:
- Loss: 2.5280
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: 8
- total_train_batch_size: 56
- 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 |
|---|---|---|---|
| 2.9998 | 0.2051 | 1 | 3.0382 |
| 3.0081 | 0.4103 | 2 | 3.0379 |
| 2.9024 | 0.6154 | 3 | 3.0356 |
| 2.9814 | 0.8205 | 4 | 3.0280 |
| 2.9813 | 1.0256 | 5 | 3.0136 |
| 2.9137 | 1.1795 | 6 | 2.9918 |
| 2.9909 | 1.3846 | 7 | 2.9426 |
| 2.8925 | 1.5897 | 8 | 2.9047 |
| 2.825 | 1.7949 | 9 | 2.8790 |
| 2.8329 | 2.0 | 10 | 2.7949 |
| 2.6496 | 2.1538 | 11 | 2.7632 |
| 2.6857 | 2.3590 | 12 | 2.7388 |
| 2.679 | 2.5641 | 13 | 2.7193 |
| 2.6802 | 2.7692 | 14 | 2.6748 |
| 2.6269 | 2.9744 | 15 | 2.6452 |
| 2.5546 | 3.1282 | 16 | 2.6286 |
| 2.574 | 3.3333 | 17 | 2.6168 |
| 2.5548 | 3.5385 | 18 | 2.6054 |
| 2.5145 | 3.7436 | 19 | 2.5952 |
| 2.452 | 3.9487 | 20 | 2.5863 |
| 2.4647 | 4.1026 | 21 | 2.5786 |
| 2.423 | 4.3077 | 22 | 2.5715 |
| 2.4104 | 4.5128 | 23 | 2.5648 |
| 2.3664 | 4.7179 | 24 | 2.5592 |
| 2.4211 | 4.9231 | 25 | 2.5536 |
| 2.4291 | 5.0769 | 26 | 2.5492 |
| 2.3475 | 5.2821 | 27 | 2.5455 |
| 2.3665 | 5.4872 | 28 | 2.5417 |
| 2.3862 | 5.6923 | 29 | 2.5387 |
| 2.3784 | 5.8974 | 30 | 2.5360 |
| 2.354 | 6.0513 | 31 | 2.5343 |
| 2.3442 | 6.2564 | 32 | 2.5321 |
| 2.3499 | 6.4615 | 33 | 2.5312 |
| 2.3312 | 6.6667 | 34 | 2.5297 |
| 2.3551 | 6.8718 | 35 | 2.5289 |
| 2.3363 | 7.0256 | 36 | 2.5289 |
| 2.3691 | 7.2308 | 37 | 2.5284 |
| 2.3267 | 7.4359 | 38 | 2.5281 |
| 2.3389 | 7.6410 | 39 | 2.5281 |
| 2.1969 | 7.8462 | 40 | 2.5280 |
Framework versions
- Transformers 4.40.2
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
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