Instructions to use opencal/opencal-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use opencal/opencal-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="opencal/opencal-base")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("opencal/opencal-base")
model = AutoModelForMultimodalLM.from_pretrained("opencal/opencal-base", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Transformers.js

How to use opencal/opencal-base with Transformers.js:

// npm i @huggingface/transformers
import { pipeline } from '@huggingface/transformers';

// Allocate pipeline
const pipe = await pipeline('image-text-to-text', 'opencal/opencal-base');

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use opencal/opencal-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "opencal/opencal-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "opencal/opencal-base",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/opencal/opencal-base

SGLang

How to use opencal/opencal-base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "opencal/opencal-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "opencal/opencal-base",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "opencal/opencal-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "opencal/opencal-base",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use opencal/opencal-base with Docker Model Runner:
```
docker model run hf.co/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.

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