Instructions to use Leng2beat/asa-global-alld-discontinuity with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Leng2beat/asa-global-alld-discontinuity with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("Leng2beat/asa-global-alld-discontinuity") model = AutoModelForMultimodalLM.from_pretrained("Leng2beat/asa-global-alld-discontinuity") - Notebooks
- Google Colab
- Kaggle
dpo-full-sft-dis5
This model was trained from scratch 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: 1e-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- 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
- num_epochs: 1
Training results
Framework versions
- Transformers 4.48.3
- Pytorch 2.6.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.4
- Downloads last month
- 24
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support