| | --- |
| | library_name: peft |
| | license: mit |
| | base_model: microsoft/Florence-2-base |
| | tags: |
| | - base_model:adapter:microsoft/Florence-2-base |
| | - lora |
| | - transformers |
| | pipeline_tag: text-generation |
| | model-index: |
| | - name: florence2_output |
| | results: [] |
| | --- |
| | |
| | <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| | should probably proofread and complete it, then remove this comment. --> |
| |
|
| | # florence2_output |
| | |
| | This model is a fine-tuned version of [microsoft/Florence-2-base](https://huggingface.co/microsoft/Florence-2-base) on an unknown dataset. |
| | It achieves the following results on the evaluation set: |
| | - Loss: 1.5346 |
| | |
| | ## 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: 4 |
| | - eval_batch_size: 4 |
| | - seed: 42 |
| | - gradient_accumulation_steps: 4 |
| | - total_train_batch_size: 16 |
| | - 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: 50 |
| | - training_steps: 500 |
| | - mixed_precision_training: Native AMP |
| |
|
| | ### Training results |
| |
|
| | | Training Loss | Epoch | Step | Validation Loss | |
| | |:-------------:|:-----:|:----:|:---------------:| |
| | | 1.7452 | 25.0 | 100 | 1.7258 | |
| | | 1.1957 | 50.0 | 200 | 1.4125 | |
| | | 1.1343 | 75.0 | 300 | 1.4571 | |
| | | 1.0557 | 100.0 | 400 | 1.5572 | |
| | | 1.0336 | 125.0 | 500 | 1.5346 | |
| |
|
| |
|
| | ### Framework versions |
| |
|
| | - PEFT 0.18.1 |
| | - Transformers 4.57.3 |
| | - Pytorch 2.9.1+cu128 |
| | - Datasets 4.4.2 |
| | - Tokenizers 0.22.2 |
