Image-Text-to-Text
PEFT
Safetensors
English
lora
vision-language
food
nutrition
calories
smolvlm2
image-to-text
causal-lm datasets
conversational
Instructions to use Unnatrathi/caloraify-lora-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Unnatrathi/caloraify-lora-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolVLM2-500M-Instruct") model = PeftModel.from_pretrained(base_model, "Unnatrathi/caloraify-lora-adapter") - Notebooks
- Google Colab
- Kaggle
File size: 599 Bytes
be768a6 | 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 | {
"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": "SmolVLMImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"max_image_size": {
"longest_edge": 512
},
"processor_class": "SmolVLMProcessor",
"resample": 1,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 2048
},
"video_sampling": {
"fps": 1,
"max_frames": 64,
"video_size": {
"longest_edge": 512
}
}
}
|