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SmolVLM-256M-Instruct-vqav2 – AI Model by danielsyahputra | AlphaNeural AI

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danielsyahputra/
SmolVLM-256M-Instruct-vqav2

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HuggingFaceTB/SmolVLM-256M-Instruct

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SmolVLM-256M-Instruct-vqav2

This model is a fine-tuned version of HuggingFaceTB/SmolVLM-256M-Instruct 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: 0.0001
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 4
optimizer: Use paged_adamw_8bit 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: 50
num_epochs: 3

Training results

Framework versions

PEFT 0.14.0
Transformers 4.47.0
Pytorch 2.5.1+cu121
Datasets 3.2.0
Tokenizers 0.21.0