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
Download preprocessor_config.json from Unnatrathi/caloraify-lora-adapter: direct link, hf CLI and curl.
- Browser
- Download file 599 Bytes
-
https://huggingface.co/Unnatrathi/caloraify-lora-adapter/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://Unnatrathi/caloraify-lora-adapter/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/Unnatrathi/caloraify-lora-adapter/resolve/main/preprocessor_config.json
599 Bytes
| { | |
| "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 | |
| } | |
| } | |
| } | |