Instructions to use Tsedee/whisper-large-v2-mn-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Tsedee/whisper-large-v2-mn-lora with PEFT:
Task type is invalid.
- Transformers
How to use Tsedee/whisper-large-v2-mn-lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Tsedee/whisper-large-v2-mn-lora", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Configuration Parsing Warning:In adapter_config.json: "peft.task_type" must be a string
whisper-large-v2-mn-lora
This model is a fine-tuned version of bayartsogt/whisper-large-v2-mn-13 on an unknown dataset.
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: 0.001
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.18.1
- Transformers 4.46.3
- Pytorch 2.12.0.dev20260330+cu128
- Datasets 3.1.0
- Tokenizers 0.20.3
- Downloads last month
- -
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for Tsedee/whisper-large-v2-mn-lora
Base model
bayartsogt/whisper-large-v2-mn-13