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Omniscience-VQA-model
aaron1141/Omniscience-VQA-model
Omniscience-VQA-model is a image-text-to-text model from aaron1141. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as other.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Image-Text-to-Text
- Library
- transformers
- License
- other
- Model type
- qwen3_5
- Access
- Public
- Created
- May 19, 2026
- Updated
- May 19, 2026
- SHA
- 14eb1746
Try a prompt
Base models
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
omniscience_vqa_5epoch_full
This model is a fine-tuned version of Qwen/Qwen3.5-9B-Base on the omniscience_vqa 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: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 0.03
- num_epochs: 5
Training results
Framework versions
- Transformers 5.6.0
- Pytorch 2.8.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
- Task
- Image-Text-to-Text
- Library
- transformers
- Type
- qwen3_5
- License
- other
- Created
- May 19, 2026
- Updated
- May 19, 2026