Instructions to use Mantis-VL/mantis-8b-idefics2-video-eval-debug_4096_regression with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mantis-VL/mantis-8b-idefics2-video-eval-debug_4096_regression with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Mantis-VL/mantis-8b-idefics2-video-eval-debug_4096_regression")# Load model directly from transformers import AutoProcessor, AutoModelForSequenceClassification processor = AutoProcessor.from_pretrained("Mantis-VL/mantis-8b-idefics2-video-eval-debug_4096_regression") model = AutoModelForSequenceClassification.from_pretrained("Mantis-VL/mantis-8b-idefics2-video-eval-debug_4096_regression") - Notebooks
- Google Colab
- Kaggle
mantis-8b-idefics2-video-eval-debug_4096_regression
This model is a fine-tuned version of HuggingFaceM4/idefics2-8b 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-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1.0
Training results
Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
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Model tree for Mantis-VL/mantis-8b-idefics2-video-eval-debug_4096_regression
Base model
HuggingFaceM4/idefics2-8b