Midi Single Instrument Mistral Transformer

• learning_rate: 0.0001
• train_batch_size: 30
• eval_batch_size: 30
• seed: 444
• gradient_accumulation_steps: 3
• total_train_batch_size: 90
• 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: 20000

Framework versions

• Transformers 4.46.2
• Pytorch 2.1.0+cu121
• Datasets 3.1.0
• Tokenizers 0.20.3

--- license: mit metrics: - accuracy tags: - mistral - midi - miditok - music - instrument pipeline_tag: audio-to-audio model-index: - name: Mistral_MidiTok_Transformer_Single_Instrument_Small results: [] --- # Mistral_MidiTok_Transformer_Single_Instrument_Small This model is trained from scratch using tokenized midi music. I have trained a MidiTok tokeniser (REMI) and its made by spliting multi-track midi into a single track. We then trained in on a small dataset. Its using the Mistral model that has been cut down quite a bit. ### What else needs to be done Update model training to use small positional embeddings for the model 1024 + a padding amount like 8 ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 30 - eval_batch_size: 30 - seed: 444 - gradient_accumulation_steps: 3 - total_train_batch_size: 90 - 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: 20000 ### Framework versions - Transformers 4.46.2 - Pytorch 2.1.0+cu121 - Datasets 3.1.0 - Tokenizers 0.20.3