How to use from
Unsloth StudioInstall Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for tachiwin/language_classification_curricular_checkpoints to start chattingUsing HuggingFace Spaces for Unsloth
# No setup required# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tachiwin/language_classification_curricular_checkpoints to start chattingLoad model with FastModel
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="tachiwin/language_classification_curricular_checkpoints",
max_seq_length=2048,
)Quick Links
language_classification_curricular_checkpoints
This model is a fine-tuned version of unsloth/whisper-large-v3-turbo 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: 0.0002
- train_batch_size: 6
- eval_batch_size: 1
- seed: 3407
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Use OptimizerNames.ADAMW_8BIT 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: 69
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.56.2
- Pytorch 2.9.0+cu126
- Datasets 3.6.0
- Tokenizers 0.22.2
- Downloads last month
- 5
Model tree for tachiwin/language_classification_curricular_checkpoints
Base model
openai/whisper-large-v3 Finetuned
unsloth/whisper-large-v3-turbo
Install Unsloth Studio (macOS, Linux, WSL)
# Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for tachiwin/language_classification_curricular_checkpoints to start chatting