docs(d3): FastFlowLM 1.0.11 -- images, 32,768 tokens, smoke test with an image

docs/docs/benchmarks/d3_results.md

@@ -7,7 +7,7 @@ nav_order: 20
 
 ## ⚡ d3-lite on the NPU
 
-Accuracy and latency of `d3:lite` (vllm-sr/d3-lite v3.1.3, package D3-Lite-NPU2) served by FastFlowLM 1.0.10.
+Accuracy and latency of `d3:lite` (vllm-sr/d3-lite v3.1.3, package D3-Lite-NPU2) served by FastFlowLM 1.0.11.
 
 > **Note:**
 > - AMD Ryzen AI MAX+ PRO 395 (Strix Halo, XDNA 2 NPU), 128 GB, Ubuntu 24.04, XRT, FLM's default NPU power mode (Performance).
@@ -44,3 +44,43 @@ First request after `flm serve` is ready: 191 ms (steady state 168 ms over HTTP)
 Rows of 2,048 tokens and more: median of 15 runs (32,768: 3). From 1,536 tokens attention runs on the NPU (below
 that the CPU is faster); the first such request also loads the attention kernel (about 0.1 s) and from then on the
 decision model holds two NPU hardware contexts instead of one.
+
+### Images
+
+Accuracy on the Decision Index public vision suite (the 10 benchmarks that can be rebuilt without gated data; 60
+questions each, images capped at 1.6 MP as the suite's `decide` engine sends them):
+
+| Benchmark | Upstream BF16 | NPU (FLM) | Same answer |
+|---|---:|---:|---:|
+| CV-Bench | 81.7% | 85.0% | 91.7% |
+| BLINK | 51.7% | 58.3% | 78.3% |
+| RealWorldQA | 68.3% | 71.7% | 88.3% |
+| CharXiv | 65.0% | 70.0% | 90.0% |
+| InfographicVQA | 78.3% | 80.0% | 90.0% |
+| Mind2Web | 61.7% | 63.3% | 83.3% |
+| KIE (CORD + FUNSD) | 91.7% | 93.3% | 98.3% |
+| Moderation (Hateful Memes) | 55.0% | 53.3% | 95.0% |
+| R-Bench-M | 31.7% | 26.7% | 71.7% |
+| MMMU-Pro vision | 16.7% | 15.0% | 58.3% |
+| **All 600** | **60.2%** | **61.7%** | **84.5%** |
+
+Paired gold accuracy, NPU minus upstream BF16: +1.50 pt (95% CI [−0.92, +3.92] pt). Image grids and input token
+counts equal upstream's on every question; the processor's pixels are within 2 grey levels of upstream's.
+
+Latency with and without an image, at the same total input length (one question, over HTTP, median of 5; 8,192
+tokens: 3 runs; 16,384 and 32,000: 1):
+
+| Input tokens | Text only (ms) | Text + one image (ms) | Image |
+|---:|---:|---:|---|
+| 516 | 266 | 483 | 640 × 480 (300 tokens) |
+| 1,028 | 448 | 658 | 640 × 480 (300 tokens) |
+| 2,052 | 830 | 1,651 | 1.6 MP (1,600 tokens) |
+| 4,100 | 1,803 | 2,641 | 1.6 MP |
+| 8,196 | 3,798 | 4,641 | 1.6 MP |
+| 16,386 | 8,733 | 9,560 | 1.6 MP |
+| 31,994 | 21,466 | 22,477 | 1.6 MP |
+
+An image costs a fixed amount on top of its tokens (decoding, resizing and the vision tower): about 0.2 s at
+640 × 480 and 0.8 s at 1.6 MP. All questions of a request share one image encoding: a 1.6 MP image with 8 questions
+takes 6.4 s, not 8 × 1.5 s. The first request with images loads the vision tower (about 40 ms) and two more NPU
+hardware contexts.

docs/docs/instructions/cli.md

@@ -368,6 +368,21 @@ See the ASR guide [here](https://fastflowlm.com/docs/models/whisper/)
 
 ---
 
+### 🧭 Decision Model (System One)
+
+`--decision <tag>` loads a decision model (`d3:lite`) next to the chat model, or alone, and serves `POST /v1/systemone`
+(text and images, up to 32,768 input tokens per question; FastFlowLM 1.0.11 or later).
+Decision models run in **Server Mode only** (`flm run d3:lite` prints how to serve it).
+
+```shell
+flm serve --decision d3:lite                 # decision model only
+flm serve qwen3.5:4b --decision d3:lite      # next to a chat model
+```
+
+See the d3 guide [here](https://fastflowlm.com/docs/models/d3/)
+
+---
+
 ## 💻 Commands Inside CLI Mode
 
 Once inside the CLI, use the following commands. System commands always start with `/` (e.g., `/help`).

docs/docs/instructions/server/index.md

@@ -27,6 +27,7 @@ FLM can keep one NPU model loaded per type at a time:
 - `asr`
 - `llm`
 - `embedding`
+- `decision` (`--decision d3:lite`, the System One API; see [Decision model](#decision-model-system-one) below)
 
 Different model types can run together (for example, one LLM and one embedding model).
 
@@ -47,6 +48,24 @@ flm serve lfm2:1.2b --port 52628
 
 
 
+## Decision Model (System One)
+
+A decision model answers typed questions about a state (choices, yes / no, scores) with a probability for every
+option, over `POST /v1/systemone`; requests may carry images (base64 data URLs). It is served next to a chat model or
+on its own:
+
+```shell
+flm serve --decision d3:lite                 # decision model only
+flm serve qwen3.5:4b --decision d3:lite      # next to a chat model
+```
+
+`GET /v1/systemone/models` lists the loaded decision model with its input limit and modalities. Requests share the
+NPU with the chat model and run one at a time, so a decision waits for a chat completion that is already running; a
+second `flm serve` for the decision model does not avoid this (the two processes contend for the NPU). The decision
+model takes one NPU hardware context, two once a prompt of 1,536 tokens or more arrives, and two more on the first
+request with images; mind this when the chat model and others already use most of the device's 16. Request format, a smoke test and latency:
+[Decision 3.0 (d3)](https://fastflowlm.com/docs/models/d3/).
+
 ## Set Context Length at Launch
 
 The default context length for each model can be found [here](https://fastflowlm.com/docs/models/). 

docs/docs/models/d3.md

@@ -14,6 +14,7 @@ parent: Models
 - **Quantization:** Q4_K
 - **Max input:** 32,768 tokens per question (longer prompts get `max_length_exceeded`; nothing is truncated). One question takes about 0.4 s at 1,024 tokens, 1.6 s at 4,096 and 23 s at 32,768; the NPU serves one request at a time
 - **Inputs:** text and images (video: not supported)
+- **FastFlowLM:** 1.0.11 or later
 
 d3 answers *System One* requests: a `state` (any JSON value) and one or more questions, each a `choice` (up to 255
 options), a `noul` (no / yes) or a `score` (2–10 ordered levels). Every option gets a probability. It is the decision
@@ -65,6 +66,54 @@ The reply on a Ryzen AI MAX+ 395 (numbers rounded to 3 places here; the server s
  "usage":{"input_tokens":342,"output_tokens":0}}
 ```
 
+### Smoke test
+
+Five commands check a new install end to end, text and images:
+
+```shell
+flm pull d3:lite                         # downloads D3-Lite-NPU2 (about 0.8 GB, vision weights included)
+flm serve --decision d3:lite             # wait for "Decision model ready: d3-lite (d3:lite), max input 32768 tokens"
+```
+
+In a second terminal:
+
+```shell
+curl -s http://127.0.0.1:52625/v1/systemone/models
+# {"object":"list","data":[{"id":"d3:lite","object":"model","api":"systemone","max_input_tokens":32768,
+#  "modalities":["text","image"],"image_max_pixels":1638400}]}
+
+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}}}}'
+# {"model":"d3-lite","answers":{"route":{"type":"choice","choice":"returns","probabilities":{"returns":0.98,...
+```
+
+A healthy install answers `"choice":"returns"` with `returns` near 0.98 in about 0.1 s.
+
+Then an image (Linux shell; the image is a 639 × 480 PNG from this repository):
+
+```shell
+curl -sLo panda.png https://raw.githubusercontent.com/ROCm/FastFlowLM/main/src/tb_files/panda.png
+printf '{"questions": {"animal": {"type": "choice", "instructions": "Which animal is in the photo?",
+  "criteria": {"dog": "A dog", "panda": "A panda", "cat": "A cat"}}},
+  "images": ["data:image/png;base64,%s"]}' "$(base64 -w0 panda.png)" > image_request.json
+curl -s http://127.0.0.1:52625/v1/systemone -H "Content-Type: application/json" -d @image_request.json
+# {"model":"d3-lite","answers":{"animal":{"type":"choice","choice":"panda","probabilities":{"dog":0.0004,"panda":0.999,...
+#  "usage":{"input_tokens":404,"output_tokens":0}}
+```
+
+On Windows (PowerShell):
+
+```powershell
+Invoke-WebRequest https://raw.githubusercontent.com/ROCm/FastFlowLM/main/src/tb_files/panda.png -OutFile panda.png
+$img = [Convert]::ToBase64String([IO.File]::ReadAllBytes("panda.png"))
+$body = '{"questions": {"animal": {"type": "choice", "instructions": "Which animal is in the photo?", "criteria": {"dog": "A dog", "panda": "A panda", "cat": "A cat"}}}, "images": ["data:image/png;base64,' + $img + '"]}'
+Invoke-RestMethod http://127.0.0.1:52625/v1/systemone -Method Post -ContentType "application/json" -Body $body | ConvertTo-Json -Depth 5
+```
+
+A healthy install answers `"choice":"panda"` with `panda` near 0.999 and 404 input tokens, in about 0.4 s (the first
+image request also loads the vision tower). `flm run d3:lite` does not start a chat; it prints how to serve the model.
+
 ### Request and response
 
 | Field | Meaning |

docs/models.md

@@ -11,7 +11,8 @@ sections:
       FastFlowLM curates the most requested families and publishes tuned manifests under `flm pull <model>`.
       We validate every build on Ryzen™ AI laptops and provide matching cards—see them [here](/docs/models/).
       The catalog already covers models spanning Qwen3.8 (27B, MTP speculative decoding), Qwen3.6 (MoE), Qwen3.5 (Vision), Gemma4 (omni), Qwen3, GPT-OSS-20B (MoE), DeepSeek-R1, Whisper
-      (Audio), Hy-MT2 (Translation), MiniCPM-V 4.7 (Vision), MedGemma (Medical), and other leading open families.
+      (Audio), Hy-MT2 (Translation), MiniCPM-V 4.7 (Vision), MedGemma (Medical), Decision 3.0 (d3-lite, the System One
+      decision model of vLLM Semantic Router), and other leading open families.
     ctas:
       - label: "View model docs"
         href: "/docs/models/"
@@ -34,6 +35,7 @@ sections:
         - "Whisper (Audio)"
         - "Hy-MT2 (Translation)"
         - "SmolVLA (Robotics)"
+        - "Decision 3.0 (System One)"
         - "MedGemma (Medical)"
         - "DeepSeek R-1"
         - "LLaMA 3.x"

src/model_list.json

@@ -831,7 +831,7 @@
                 "file_url": "https://huggingface.co/api/models/FastFlowLM/D3-Lite-NPU2/tree/main",
                 "ms_url": "https://modelscope.cn/models/amd/D3-Lite-NPU2",
                 "size": 800000000,
-                "flm_min_version": "1.0.10",
+                "flm_min_version": "1.0.11",
                 "files": [
                     "config.json",
                     "model.q4nx",