D3-Mini-NPU2

vllm-sr/d3-mini v3.1.3 (Decision 3.0, the decision model of vLLM Semantic Router; Qwen3.5-4B text tower) for AMD Ryzen AI NPUs, served by FastFlowLM 1.0.11 or later. It answers System One requests -- choice, yes / no and score questions about a state, optionally with images, with a probability for every option -- over the same POST /v1/systemone API as the upstream package. Up to 32,768 input tokens per question. Video is not supported (a request with videos is refused with HTTP 422).

flm serve --decision d3:mini          # optionally next to a chat model: flm serve qwen3.5:4b --decision d3:mini
curl -s http://127.0.0.1:52625/v1/systemone -H "Content-Type: application/json" \
  -d '{"state": "The order arrived damaged.", "questions": {"route": {"type": "choice",
       "criteria": {"returns": "Refunds and replacements", "billing": "Payments", "general": null}}}}'

Images go in an images list of base64 data URLs (PNG, JPEG or WebP; each at most 8 MB and 16 MP, resized to at most 1.6 MP), shared by every question of the request. d3:lite (0.8B) and d3:nano (2B) are the smaller siblings.

What was changed

Weights: the Qwen3.5-4B text tower quantized to FastFlowLM's Q4NX format: every projection Q4_K with GPTQ-calibrated rounding (calibration: 300 synthetic System One questions of up to 2k tokens, none from the Decision Index), token embedding Q8_0, norms and the Gated DeltaNet parameters unquantized. vision_weight.q4nx holds the vision tower (identical to Qwen3.5-4B's, merger included), used for requests with images.
Unchanged: readout.safetensors, decision_config.json, tokenizer.json, tokenizer_config.json, chat_template.jinja and LICENSE are the upstream files byte for byte (MANIFEST.sha256).
Same contract: prompts, token ids and answer objects follow the upstream runtime.

Accuracy (against the upstream BF16 model)

	this package on the NPU	upstream BF16
Decision Index public-suite subset, mean benchmark score (1,600 requests, 42 benchmarks)	62.8%	63.8%
Top-1 agreement with BF16 on the same requests (12,656 choice answers)	97.0%	--
Final-norm hidden state vs BF16, cosine (725 text answers, median)	0.9994	--
Same top answer as BF16 (735 text answers / 60 image answers)	96.3% / 96.7%	--
Decision Index public vision suite subset, accuracy (600 questions, 10 benchmarks)	69.2%	71.7%
Top-1 agreement with BF16 on the same image questions	89.8%	--

Mean benchmark score, this package minus BF16: -0.99 pt (standard error 0.57 pt; about 37 requests per benchmark, so single benchmarks are noisy). The subset is a seeded round-robin sample over the suite's benchmarks, not the full suite. The vision subset is 60 seeded questions from each of 10 benchmarks (the suite without the gated Winoground); paired accuracy, BF16 minus this package: +2.5 pt (95% CI +0.5 to +4.5 pt), so images cost a little accuracy (largest gaps on Mind2Web, MMMU-Pro, CV-Bench and CharXiv, each about 60 questions) while the answers agree with BF16 on 89.8% of the questions.

Speed (AMD Ryzen AI MAX+ PRO 395 (Strix Halo, XDNA 2 NPU), 128 GB, Ubuntu 24.04, XRT, FastFlowLM 1.0.11)

request	median latency
1 question, 128 tokens	473 ms
1 question, 330 tokens	536 ms
1 question, 1,024 tokens	1.3 s
1 question, 4,096 tokens	5.3 s
1 question, 16,116 tokens	25.9 s
1 question, 31,545 tokens	62.9 s
1 question, 32,768 tokens	66.5 s
4 questions, 330 tokens each	1.8 s
16 questions, 330 tokens each	6.6 s
1 question + a 1024 x 768 image (876 tokens, HTTP)	2.1 s
1 question + two images (HTTP)	3.8 s
1 question + a 2000 x 1500 image (downscaled to 1.6 MP, HTTP)	4.4 s
8 questions + one image (one image encoding, HTTP)	10.8 s

From 1,536 tokens attention runs on the NPU's attention kernel (below that the CPU is as fast or faster). Prompts over 32,768 tokens are answered with max_length_exceeded (nothing is truncated). Peak resident memory of the server at 32,768 tokens: 8.6 GB. Text rows were measured at the engine (HTTP adds about a millisecond); the image rows over HTTP, one question unless noted, median of 3, on an otherwise idle machine.