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Qwen2.5-VL-3B-Instruct-SFT – AI Model by Liang0223 | AlphaNeural AI
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Liang0223
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Qwen2.5-VL-3B-Instruct-SFT
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transformers
tensorboard
safetensors
qwen2_5_vl
image-text-to-text
llama-factory
full
generated_from_trainer
conversational
Qwen/Qwen2.5-VL-3B-Instruct
finetune
other
text-generation-inference
endpoints_compatible
us
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sft-5e-5-256
This model is a fine-tuned version of
Qwen/Qwen2.5-VL-3B-Instruct
on the wethink 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: 1
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 8
gradient_accumulation_steps: 32
total_train_batch_size: 256
total_eval_batch_size: 64
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 1.0
Training results
Framework versions
Transformers 4.49.0
Pytorch 2.8.0+cu128
Datasets 3.2.0
Tokenizers 0.21.0
