Karti-Small-VL-4B · v1

A 4B vision model that knows when it can't read something.

Built for local agents that look at a thing and then call a tool about it — read a panel, check a camera frame, act on a screen. The failure that matters there isn't a wrong caption; it's a confidently invented identifier. v1 is trained to hit that boundary and decline instead.

Parameters	4.66 B (4,659,865,088)
Base	Qwen/Qwen3.5-4B @ 851bf6e8
Precision	BF16 · 9.3 GB · merged weights, no adapter
Context	32,768
Tools	native, qwen3_xml parser
Faster build	NVFP4 — 2.5×, Blackwell only

Results

Same endpoint, same rows, same decode path.

	base	v1
Invented-identifier rate	0.378	0.023 — 16× fewer
Panel reading	0.595	0.967
GUI grounding, ScreenSpot-v2 · n=1271	0.718	0.907
⤷ small targets · n=426	0.580	0.883
Unparseable click points	48/1272	9/1272
General vision, curated · n=220	0.727	0.732
False-refusal, real photos · n=600	0.220	0.227

ScreenSpot-v2 and the general-vision slice were held out. On the two hardest abstention families the base scored zero; v1 scores 101/112 and 117/119.

Scope, honestly: part of the grounding gain is learning the normalised 0–1000 coordinate convention shared with the training set — real, but not "38% better at seeing UI". And v1 reads photographs no better than the base; what changed is that it stops over-refusing them. The legibility gain is on rendered panels and screens.

Speed — NVIDIA DGX Spark (GB10)

vLLM 0.27.1, BF16, 32k context, FP8 KV cache, temperature 0, single stream.

tokens	tok/s
128	21.0
512	21.1

Fits in ~20 GB. For 2.5× throughput at 3.7 GB, use the NVFP4 build (needs Blackwell, sm_120+).

Use

from transformers import AutoProcessor, AutoModelForImageTextToText

m = AutoModelForImageTextToText.from_pretrained(
    "KartiOS/Karti-Small-VL-4B", dtype="auto", device_map="auto")
p = AutoProcessor.from_pretrained("KartiOS/Karti-Small-VL-4B")

vllm serve KartiOS/Karti-Small-VL-4B \
  --max-model-len 32768 --kv-cache-dtype fp8 \
  --enable-auto-tool-choice --tool-call-parser qwen3_xml \
  --default-chat-template-kwargs '{"enable_thinking": false}'

Pin a revision — main moves. revision="pre-v1" is the untrained baseline.

How it was trained

BF16 LoRA (r16, α32, lr 5e-5, 75 steps) merged into these weights. 32.5 M trainable parameters across 248 language modules; the vision tower is frozen and proven frozen — all 297 vision tensors are byte-identical to the base.

Corpus: 3,569 rows — an authored legibility ladder that places text exactly at the readable/unreadable boundary, plus VizWiz and GUI-Odyssey.

The decisive parameter was the share of the corpus whose answer is "I can't read this." At 29.1% the model learns the word, not the judgement: 0.955 on the lane, then refusing 14 of 22 answerable photographs. At 14.4% it learns the judgement. That threshold — not the learning rate, not the step count — is what made v1 work.

Serve the merged weights, not a LoRA adapter. vLLM 0.27.1 applies this adapter incompletely — about 0.19 absolute ScreenSpot accuracy, silently. Qwen3.5 packs its linear-attention projections and this adapter targets those sub-modules. The adapter is not published for that reason.

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From Lumbridge. Sibling text model: Karti-Small-RSI-3B.