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 processor_config.json from Unnatrathi/caloraify-lora-adapter: direct link, hf CLI and curl.
- Browser
- Download file 67 Bytes
-
https://huggingface.co/Unnatrathi/caloraify-lora-adapter/resolve/main/processor_config.json
- Command line
-
hf download hf://Unnatrathi/caloraify-lora-adapter/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/Unnatrathi/caloraify-lora-adapter/resolve/main/processor_config.json
67 Bytes
| { | |
| "image_seq_len": 64, | |
| "processor_class": "SmolVLMProcessor" | |
| } | |