Instructions to use fatsam13/beforeat-food-nutrition-vision-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use fatsam13/beforeat-food-nutrition-vision-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-VL-4B-Instruct") model = PeftModel.from_pretrained(base_model, "fatsam13/beforeat-food-nutrition-vision-lora") - Notebooks
- Google Colab
- Kaggle
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Download RELEASE_NOTES_v2.md from fatsam13/beforeat-food-nutrition-vision-lora: direct link, hf CLI and curl.
- Browser
- Download file 934 Bytes
-
https://huggingface.co/fatsam13/beforeat-food-nutrition-vision-lora/resolve/main/RELEASE_NOTES_v2.md
- Command line
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hf download hf://fatsam13/beforeat-food-nutrition-vision-lora/RELEASE_NOTES_v2.md
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curl -L -o RELEASE_NOTES_v2.md https://huggingface.co/fatsam13/beforeat-food-nutrition-vision-lora/resolve/main/RELEASE_NOTES_v2.md
934 Bytes
Version 2 Release Notes
Version 2 is one multi-task LoRA adapter, distributed as matching PEFT and GGUF representations. It replaces the current adapter for new integrations while the v1 GGUF remains available for rollback.
Changes
- Improves Food-101 strict accuracy from 86.80% to 87.60% on 500 held-out images.
- Adds experimental visible-ingredient and rough-gram JSON output.
- Keeps nutrition calculation outside the model through USDA FoodData Central mappings and application rules.
- Preserves rank 8, alpha 16, and the seven original target-module families.
- Validated with the existing Unsloth
UD-Q4_K_XLbase andmmproj-F16.gguf.
iOS Migration
Update only the adapter download filename to:
beforeat-food-nutrition-vision-lora-v2-f16.gguf
Continue to reuse the existing base GGUF and projector, and apply the adapter at
scale 1.0. Do not load the historical v1 adapter at the same time.