opencal/opencal-base

OpenCal Base

OpenCal Base is a LFM2.5-VL-450M fine-tune that reads food photos (and text) and outputs structured ingredient + gram-weight extractions aligned to the USDA food database. It is exported to ONNX for the @huggingface/transformers.js browser runtime (WebGPU / WASM / CPU).

Model details

Field	Value
Base model	LiquidAI/LFM2.5-VL-450M
Fine-tune	LoRA, OpenCal v6 (gram-weight + kcal/macro target)
Architecture	Lfm2VlForConditionalGeneration (model_type: lfm2_vl)
Runtime	ONNX / transformers.js (WebGPU, WASM, CPU)
License	Apache-2.0

Fine-tune summary

LoRA on top of the frozen base, merged weights. Target format is a JSON list of {name, grams, kcal, protein_g, carbs_g, fat_g} extracted per item, with grams normalized per 100 g and macros sourced from USDA data. The model is used by the OpenCal app to turn a photo or a text description of a meal into per-item nutrition.

Benchmark results (OpenCal internal evals)

• F101 (full meals, median gold kcal ≈ 293): kcal MAE 243, WAPE 41.5%, within-50% of gold 70.4%.
• N5k (lab samples, median gold kcal ≈ 43): ingredient-identity recall baseline 76.4%, this model 84.4% (reference LLM 73.6%).

Usage (transformers.js)

import { AutoModelForImageTextToText, AutoProcessor, RawImage } from '@huggingface/transformers';

const processor = await AutoProcessor.from_pretrained('OpenCal/opencal-base');
const model = await AutoModelForImageTextToText.from_pretrained('OpenCal/opencal-base', { dtype: 'auto', device: 'webgpu' });

const image = await RawImage.fromURL('https://example.com/meal.jpg');
const texts = ['List the ingredients and their amounts in grams'];

const { inputs } = await processor(image, texts, { return_tensor: false });
const { logits } = await model(inputs);
const decoded = processor.batch_decode(logits[1].id); // [1] for single sample
console.log(decoded[0]);

File layout (ONNX)

The model is split into the standard LFM2.5-VL ONNX components. The suffix of each weight file selects the runtime/precision:

File	Precision	Use
embed_tokens{,_fp16,_q4,_quantized}.onnx	fp32 / fp16 / q4 / q8	input embeddings (input_ids → inputs_embeds)
decoder_model_merged{,_fp16,_q4,_q4f16}.onnx	fp32 / fp16 / q4 / q4f16	text decoder (main body)
vision_encoder{,_fp16,_q4,_quantized}.onnx	fp32 / fp16 / q4 / q8	vision encoder

• q4f16 — MatMulNBits q4 weights with fp16 scales / KV cache (WebGPU production path).
• quantized (= q8, MatMulNBits) — WASM path.
• fp16 — 16-bit WebGPU path.

tokenizer.json, preprocessor_config.json, config.json, and generation_config.json are at the repository root.

Citation / base model

This model derives from LiquidAI/LFM2.5-VL-450M. Please see the base repository for the original weights and paper.