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vlmvector_qwen25vl_train_multi_layer_distill_AOP_pooling_layer8_ablation_1230 – AI Model by zsgvivo | AlphaNeural AI
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vlmvector_qwen25vl_train_multi_layer_distill_AOP_pooling_layer8_ablation_1230
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transformers
safetensors
qwen2_5_vl
image-text-to-text
generated_from_trainer
conversational
Qwen/Qwen2.5-VL-3B-Instruct
finetune
text-generation-inference
endpoints_compatible
us
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vlmvector_qwen25vl_train_multi_layer_distill_AOP_pooling_layer8_ablation_1230
This model is a fine-tuned version of
Qwen/Qwen2.5-VL-3B-Instruct
on the None 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: 5e-05
train_batch_size: 64
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 16
total_train_batch_size: 1024
total_eval_batch_size: 128
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
lr_scheduler_warmup_steps: 100
training_steps: 5000
Training results
Framework versions
Transformers 4.52.3
Pytorch 2.7.1
Datasets 3.3.0
Tokenizers 0.21.4
