Image-to-Text
Transformers
Safetensors
Arabic
lfm2_vl
image-text-to-text
arabic
vlm
image-captioning
siglip2
lfm2-vl
emhotob
Instructions to use oddadmix/Nawah-VL-25M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use oddadmix/Nawah-VL-25M 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="oddadmix/Nawah-VL-25M")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("oddadmix/Nawah-VL-25M") model = AutoModelForMultimodalLM.from_pretrained("oddadmix/Nawah-VL-25M", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 788 Bytes
af27206 | 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 30 31 32 33 34 35 36 37 38 39 | {
"image_processor": {
"do_image_splitting": false,
"do_normalize": true,
"do_pad": true,
"do_rescale": true,
"do_resize": true,
"downsample_factor": 2,
"encoder_patch_size": 16,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "Lfm2VlImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"max_image_tokens": 64,
"max_num_patches": 256,
"max_pixels_tolerance": 2.0,
"max_tiles": 10,
"min_image_tokens": 32,
"min_tiles": 2,
"resample": 2,
"rescale_factor": 0.00392156862745098,
"return_row_col_info": false,
"size": {
"height": 512,
"width": 512
},
"tile_size": 512,
"use_thumbnail": false
},
"processor_class": "Lfm2VlProcessor"
}
|