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florence2-pitvqa-finetuned – AI Model by mmrech | AlphaNeural AI
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mmrech
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florence2-pitvqa-finetuned
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peft
safetensors
adapter
lora
transformers
text-generation
microsoft/Florence-2-base
mit
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florence2-pitvqa-finetuned
This model is a fine-tuned version of
microsoft/Florence-2-base
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 0.7384
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: 2
eval_batch_size: 2
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 8
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: linear
lr_scheduler_warmup_steps: 20
training_steps: 200
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
4.1616
6.25
50
3.7871
1.696
12.5
100
1.3849
1.0616
18.75
150
0.8380
0.9614
25.0
200
0.7384
Framework versions
PEFT 0.18.1
Transformers 4.57.3
Pytorch 2.9.1+cu128
Datasets 4.4.2
Tokenizers 0.22.2
