Image-to-Text
Transformers
Safetensors
PEFT
English
vision-language
image-captioning
SmolVLM
LoRA
QLoRA
COCO
accelerate
Instructions to use Amirhossein75/VLM-Image-Captioning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Amirhossein75/VLM-Image-Captioning with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="Amirhossein75/VLM-Image-Captioning")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Amirhossein75/VLM-Image-Captioning", device_map="auto") - PEFT
How to use Amirhossein75/VLM-Image-Captioning with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
File size: 486 Bytes
5ed07e6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"do_convert_rgb": true,
"do_image_splitting": true,
"do_normalize": true,
"do_pad": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "Idefics3ImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"max_image_size": {
"longest_edge": 384
},
"processor_class": "Idefics3Processor",
"resample": 1,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 1536
}
}
|