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---
library_name: transformers
tags:
- generated_from_trainer
- music
metrics:
- accuracy
datasets:
- sandernotenbaert/lmd_matched
training_config:
vocab_size: 30000
hidden_size: 256
intermediate_size: 512
num_hidden_layers: 4
num_attention_heads: 4
num_key_value_heads: 4
sliding_window: 4
max_position_embeddings: 1024
pad_token_id: 0
bos_token_id: 1
eos_token_id: 2
pipeline_tag: other
model-index:
- name: OKAI-midi-gen-v-001
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. -->
# OKAI-midi-gen-v-001
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 10.1912
- Accuracy: 0.0008
## Model description
First test with small subset on M1Pro. Generates valid files, notes very clustered with long gaps
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
training_config:
vocab_size: 30000
hidden_size: 256
intermediate_size: 512
num_hidden_layers: 4
num_attention_heads: 4
num_key_value_heads: 4
sliding_window: 4
max_position_embeddings: 1024
pad_token_id: 0
bos_token_id: 1
eos_token_id: 2
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 16
- seed: 444
- gradient_accumulation_steps: 3
- total_train_batch_size: 24
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.3
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-------:|:----:|:--------:|:---------------:|
| 10.2727 | 3.2283 | 100 | 0.0000 | 10.3284 |
| 9.7582 | 6.4565 | 200 | 0.0026 | 10.0966 |
| 9.2052 | 9.6848 | 300 | 0.0037 | 9.9513 |
| 8.8216 | 12.9130 | 400 | 0.0034 | 9.9538 |
| 8.406 | 16.1304 | 500 | 0.0029 | 9.9524 |
| 7.8326 | 19.3587 | 600 | 0.0021 | 9.9458 |
| 7.1956 | 22.5870 | 700 | 0.0017 | 9.9864 |
| 6.5659 | 25.8152 | 800 | 0.0015 | 9.9258 |
| 5.9719 | 29.0326 | 900 | 0.0015 | 9.9710 |
| 5.4031 | 32.2609 | 1000 | 0.0011 | 9.9116 |
| 4.9784 | 35.4891 | 1100 | 0.0012 | 9.9819 |
| 4.6684 | 38.7174 | 1200 | 0.0009 | 10.0142 |
| 4.3184 | 41.9783 | 1300 | 10.0483 | 0.0010 |
| 4.1251 | 45.1957 | 1400 | 10.0964 | 0.0008 |
| 3.909 | 48.4239 | 1500 | 10.1322 | 0.0009 |
| 3.7535 | 51.6522 | 1600 | 10.1587 | 0.0009 |
| 3.681 | 54.8804 | 1700 | 10.1785 | 0.0008 |
| 3.688 | 58.0978 | 1800 | 10.1871 | 0.0008 |
| 3.6685 | 61.3261 | 1900 | 10.1912 | 0.0008 |
| 3.6326 | 64.5543 | 2000 | 10.1912 | 0.0008 |
### Framework versions
- Transformers 4.52.3
- Pytorch 2.6.0
- Datasets 3.6.0
- Tokenizers 0.21.1 |