---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- /content/processed_output/clartts_data.jsonl
model-index:
- name: content/finetuned_model
  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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.13.0.dev0`
```yaml
base_model: /content/SparkTTS-Finetune/pretrained_models/Spark-TTS-0.5B/LLM
load_in_4bit: false
load_in_8bit: false

trust_remote_code: true
strict: false

datasets:
  - path: /content/processed_output/clartts_data.jsonl
    type: completion

dataset_prepared_path:
val_set_size: 0.05
output_dir: /content/finetuned_model

sequence_len: 1024
sample_packing: false
eval_sample_packing: false
pad_to_sequence_len: true

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 3
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false

early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 50
xformers_attention:
flash_attention: false

warmup_steps: 10
evals_per_epoch: 1
save_steps: 200
debug:
deepspeed:
weight_decay: 0.0

```

</details><br>

# content/finetuned_model

This model was trained from scratch on the /content/processed_output/clartts_data.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 4.4637
- Memory/max Active (gib): 7.2
- Memory/max Allocated (gib): 7.2
- Memory/device Reserved (gib): 7.62

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.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
- training_steps: 1016

### Training results

| Training Loss | Epoch | Step | Validation Loss | Active (gib) | Allocated (gib) | Reserved (gib) |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:---------------:|:--------------:|
| No log        | 0     | 0    | 11.8503         | 3.1          | 3.1             | 3.2            |
| 4.7248        | 1.0   | 339  | 4.6423          | 7.2          | 7.2             | 7.67           |
| 4.3688        | 2.0   | 678  | 4.4637          | 7.2          | 7.2             | 7.62           |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.7.1+cu118
- Datasets 4.4.1
- Tokenizers 0.22.1