This is an unofficial community conversion. It is not affiliated with or endorsed by the original Food-R1 authors. The source model is zy12123/Food-R1.

Food-R1 is a Qwen3-VL image-to-text model that returns structured visual food and nutrition estimates. The main model and multimodal projector are separate; both are required for image inference.

Safety and scope

Nutritional outputs are visual estimates, not measurements. Quantization drift measures conversion behaviour relative to the converted BF16 reference; it is not ground-truth nutrition accuracy. A single image cannot reliably expose hidden oil, ingredients, sauces, preparation methods, or portion depth.

Do not use this model as the sole basis for insulin dosing, allergy safety, eating-disorder treatment, or clinical nutrition decisions. Validate the bounded JSON in the application and involve an appropriate professional for health-critical decisions.

Snapdragon performance was not directly measured. Any Windows ARM64 or Snapdragon suitability statement is a memory-fit estimate, not a benchmark.

Recommendations

Main model + projector	Recommendation
Q6_K + F16 projector	Provisional default based on this limited smoke benchmark.
Q5_K_M + F16 projector	Lower-memory fallback requiring strict application validation.
Q8_0 + F16 projector	Largest likely-to-fit option; it is not automatically the best.
Q4_K_M + F16 projector	Experimental and not recommended for nutrition estimation.

The F16 projector is primary. The optional mmproj-Food-R1-Q8_0-mixed.gguf is explicitly mixed Q8_0/F16, not pure Q8_0: 89 tensors are Q8_0, 27 are F16, and 236 are F32.

Release artifacts

Path	Quantization	Bytes
`output/Food-R1-BF16.gguf`	BF16 reference	16,388,044,832
`output/Food-R1-Q8_0.gguf`	Q8_0	8,709,519,392
`output/Food-R1-Q6_K.gguf`	Q6_K	6,725,900,320
`output/Food-R1-Q5_K_M.gguf`	Q5_K_M	5,851,113,504
`output/Food-R1-Q4_K_M.gguf`	Q4_K_M	5,027,784,736
`output/mmproj-Food-R1-F16.gguf`	F16	1,159,029,760
`output/mmproj-Food-R1-Q8_0-mixed.gguf`	mixed Q8_0/F16	752,289,664

Run sha256sum -c checksums.sha256 from the repository root.

Verified deployment benchmark

The final bounded benchmark used an NVIDIA L4, pinned llama.cpp commit 69e62fc77c911da169cc8726b490028d53bb90fe, ten Wikimedia Commons images, the F16 projector, a fresh server per main quantization, one request at a time, temperature 0, seed 42, 4,096 context tokens, 1,024 image tokens, at most 768 output tokens, and no prompt caching.

Main	Requests	Image encoded	Valid bounded JSON	Mean latency	Generate tok/s	Peak VRAM
Q6_K	10	10	10	10.42 s	33.95	8,296 MiB
Q5_K_M	10	10	10	10.03 s	38.96	7,772 MiB
Q8_0	10	10	10	12.47 s	27.86	10,044 MiB

Overall gates: 30/30 image ingestion, 30/30 valid JSON, 30/30 within schema bounds, zero crashes, and zero exact-maximum saturation flags.

The original unbounded 100-response conversion benchmark was reconstructed from raw server responses: 100/100 images ingested, 100/100 JSON responses parsed, and zero crashes. Nine responses were catastrophic under the published thresholds; all nine reproduced exactly in fresh-server reruns. These failures are preserved in the public data.

llama-server

The following flags were verified against the pinned binary:

bash

llama-server \
  -m output/Food-R1-Q6_K.gguf \
  --mmproj output/mmproj-Food-R1-F16.gguf \
  --ctx-size 4096 \
  --parallel 1 \
  --gpu-layers all \
  --image-min-tokens 1024 \
  --image-max-tokens 1024 \
  --jinja \
  --no-cache-prompt \
  --host 127.0.0.1 \
  --port 8080

Submit one image per OpenAI-compatible chat-completions request and provide tests/nutrition_safe.schema.json as a strict JSON schema.

llama-mtmd-cli

bash

llama-mtmd-cli \
  -m output/Food-R1-Q6_K.gguf \
  --mmproj output/mmproj-Food-R1-F16.gguf \
  --image meal.jpg \
  -p "Analyze this meal image. Identify the visible foods and estimate portion mass, calories, protein, carbohydrates, fat and fibre. State important uncertainties. Return valid JSON only." \
  --ctx-size 4096 \
  --n-predict 768 \
  --image-min-tokens 1024 \
  --image-max-tokens 1024 \
  --temp 0 \
  --seed 42 \
  --jinja \
  --json-schema-file tests/nutrition_safe.schema.json \
  --no-warmup

Reproduction and audit

Run bash scripts/run_deployment_benchmark.sh for the 30-request gate, python scripts/test_schema_bounds.py for the 15 schema regressions, and python scripts/inspect_gguf.py for pinned GGUF metadata validation.

See CONVERSION_REPORT.md, CONVERSION_SMOKE_BENCHMARK.md, FINAL_PUBLICATION_AUDIT.md, ATTRIBUTION.md, manifest.json, and benchmark/drift_summary.json for verified details.

AKMESSI/Food-R1-GGUF

Food-R1 GGUF — Unofficial Community Conversion