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---
library_name: peft
license: apache-2.0
base_model: HuggingFaceTB/SmolVLM-256M-Instruct
tags:
- base_model:adapter:HuggingFaceTB/SmolVLM-256M-Instruct
- lora
- transformers
- image
- gui
model-index:
- name: SmolVLM-256M-ScreenTask
results: []
datasets:
- macpaw-research/Screen2AX-Task
language:
- en
pipeline_tag: image-text-to-text
---
<!-- 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. -->
# SmolVLM-256M-ScreenTask
This model is a fine-tuned version of [HuggingFaceTB/SmolVLM-256M-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM-256M-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8402
## 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: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.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: 0.35
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.857 | 0.0599 | 20 | 2.7762 |
| 1.8633 | 0.1199 | 40 | 1.8233 |
| 1.1164 | 0.1798 | 60 | 1.0640 |
| 0.8947 | 0.2397 | 80 | 0.8909 |
| 0.8471 | 0.2996 | 100 | 0.8402 |
### Framework versions
- PEFT 0.18.0
- Transformers 4.57.3
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.1 |