---
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-pitvqa-finetuned
  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-pitvqa-finetuned

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: 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