See axolotl config
axolotl version: 0.8.0
base_model: /root/anhnct/Spark-TTS-finetune/extend_vocab_pretrained/LLM
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
trust_remote_code: true
strict: false
datasets:
- path: .
data_files: ["/root/anhnct/Spark-TTS-finetune/PROMPTS/product_ft_data/elevenlab_dataset_3.jsonl", "/root/anhnct/Spark-TTS-finetune/PROMPTS/product_ft_data/elevenlab_dataset_4.jsonl", "/root/anhnct/Spark-TTS-finetune/PROMPTS/product_ft_data/elevenlab_dataset_reflex.jsonl", "/root/anhnct/Spark-TTS-finetune/PROMPTS/product_ft_data/elevenlab_slow.jsonl", "/root/anhnct/Spark-TTS-finetune/PROMPTS/product_ft_data/hf_song_ngu.jsonl", "/root/anhnct/Spark-TTS-finetune/PROMPTS/product_ft_data/LibriTTS.jsonl"]
type: completion
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/Simp_22_1_2026
sequence_len: 2048
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 8
num_epochs: 10
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: false
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 50
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 1
save_steps: 10000
save_total_limit: 100
debug:
deepspeed:
weight_decay: 0.0
outputs/Simp_22_1_2026
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 5.3568
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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch_fused 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: 10
- num_epochs: 10.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.0005 | 1 | 5.6361 |
| 4.5777 | 1.0 | 2216 | 5.3235 |
| 4.5116 | 2.0 | 4432 | 5.3313 |
| 4.4611 | 3.0 | 6648 | 5.3390 |
| 4.4496 | 4.0 | 8864 | 5.3471 |
| 4.4141 | 5.0 | 11080 | 5.3521 |
| 4.4031 | 6.0 | 13296 | 5.3541 |
| 4.4174 | 7.0 | 15512 | 5.3562 |
| 4.4071 | 8.0 | 17728 | 5.3561 |
| 4.4179 | 9.0 | 19944 | 5.3567 |
| 4.3882 | 10.0 | 22160 | 5.3568 |
Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.4
- Downloads last month
- 1