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Omniscience-VQA-model – AI Model by aaron1141 | AlphaNeural AI
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Omniscience-VQA-model
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transformers
safetensors
qwen3_5
image-text-to-text
llama-factory
full
generated_from_trainer
conversational
Qwen/Qwen3.5-9B-Base
finetune
other
endpoints_compatible
us
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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
