Instructions to use Mantis-VL/intern_vl_25_llava_next_700k_pretrain_packing_4096 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mantis-VL/intern_vl_25_llava_next_700k_pretrain_packing_4096 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Mantis-VL/intern_vl_25_llava_next_700k_pretrain_packing_4096", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Mantis-VL/intern_vl_25_llava_next_700k_pretrain_packing_4096", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
intern_vl_25_llava_next_700k_pretrain_packing_4096
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: 4e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 8
- 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 4.45.0
- Pytorch 2.5.1+cu124
- Datasets 2.18.0
- Tokenizers 0.20.3
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