take away performance enhancement

docs/user-guide/dine-in/get-started.md

@@ -60,7 +60,7 @@ cd ../dine-in
 
 > **Note:** Only needed once. Model files are shared between Dine-In and Take-Away.
 
-This downloads Qwen2.5-VL-7B-Instruct (~7 GB) and converts it to OpenVINO™ INT8 format. This is only needed once — the model files are shared with Take-Away.
+This downloads MiniCPM-V-4.5 and converts it to OpenVINO™ INT4 format. This is only needed once — the model files are shared with Take-Away.
 
 ### Step 4: Prepare Test Data
 

docs/user-guide/dine-in/get-started/system-requirements.md

@@ -24,7 +24,7 @@ Hardware, software, and network requirements for deploying Dine-In Order Accurac
 | GPU       | Intel® Data Center GPU (for concurrent validation) |
 | Storage   | 200 GB NVMe SSD                                    |
 
-**GPU VRAM guidance:** The Qwen2.5-VL-7B INT8 model requires ~8 GB of VRAM.
+**GPU VRAM guidance:** The MiniCPM-V-4.5 INT4 model requires ~5 GB of VRAM.
 The default `cache_size=4` reserves an additional 4 GB VRAM for the KV cache. Total VRAM needed
 is around 12 GB, which fits in an Intel® Arc™ A770 16 GB. On **integrated GPU** (iGPU)
 platforms such as Wildcat Lake and Meteor Lake, the KV cache is drawn from **system RAM**

docs/user-guide/dine-in/how-it-works.md

@@ -36,7 +36,7 @@ This document provides a comprehensive technical overview of the system architec
           ┌─────────────────┐     ┌─────────────────┐
           │                 │     │                 │
           │   OVMS VLM      │     │   Semantic      │
-          │   (Qwen2.5-VL)  │     │   Service       │
+          │  (MiniCPM-V4.5) │     │   Service       │
           │   Port 8000     │     │   Port 8080     │
           │                 │     │                 │
           └─────────────────┘     └─────────────────┘

docs/user-guide/dine-in/index.md

@@ -30,7 +30,7 @@ In a full-service restaurant:
 
 | Feature                      | Description                                                                      |
 | ---------------------------- | -------------------------------------------------------------------------------- |
-| **Zero-Training Deployment** | Uses pre-trained Qwen2.5-VL-7B model - no fine-tuning required                   |
+| **Zero-Training Deployment** | Uses pre-trained MiniCPM-V-4.5 model - no fine-tuning required                   |
 | **Semantic Matching**        | Fuzzy item matching handles naming variations (e.g., "Big Mac" ↔ "Maharaja Mac") |
 | **Real-Time Validation**     | Sub-15-second end-to-end latency for operational efficiency                      |
 | **Circuit Breaker Pattern**  | Fault-tolerant with automatic service recovery                                   |

docs/user-guide/get-started/advanced.md

@@ -66,7 +66,8 @@ LOG_LEVEL=INFO
 # Service Endpoints
 # =============================================================================
 OVMS_ENDPOINT=http://ovms-vlm:8000
-OVMS_MODEL_NAME=Qwen/Qwen2.5-VL-7B-Instruct
+OVMS_MODEL_NAME=openbmb/MiniCPM-V-4_5-int4
+VLM_PRECISION=int4           # must match the -int4 suffix in OVMS_MODEL_NAME
 SEMANTIC_SERVICE_ENDPOINT=http://semantic-service:8080
 API_TIMEOUT=60
 ```
@@ -106,7 +107,10 @@ API_TIMEOUT=60
 # =============================================================================
 VLM_BACKEND=ovms
 OVMS_ENDPOINT=http://ovms-vlm:8000
-OVMS_MODEL_NAME=Qwen/Qwen2.5-VL-7B-Instruct
+OVMS_MODEL_NAME=openbmb/MiniCPM-V-4_5-int4
+VLM_PRECISION=int4           # must match the -int4 suffix in OVMS_MODEL_NAME
+VLM_IMAGE_MAX_SIZE=448       # square canvas size submitted to the VLM
+VLM_ENABLE_THINKING=false    # keep MiniCPM's <think> reasoning off
 OPENVINO_DEVICE=GPU          # 'GPU', 'CPU', or 'AUTO'
 
 # =============================================================================

docs/user-guide/index.md

@@ -52,7 +52,7 @@ Optimized for validating food trays at serving stations before delivery to table
 - REST API for POS integration
 - Gradio web interface for manual validation
 - Hybrid semantic matching
-- Zero-training deployment with pre-trained Qwen2.5-VL-7B model
+- Zero-training deployment with pre-trained MiniCPM-V-4.5 model
 
 #### Use Case
 
@@ -109,7 +109,7 @@ Optimized for high-throughput drive-through environments with multiple camera st
 
 ### VLM Backend (OVMS)
 
-Both applications use OpenVINO™ Model Server with Qwen2.5-VL for vision-language inference.
+Both applications use OpenVINO™ Model Server with MiniCPM-V-4.5 (INT4) for vision-language inference.
 
 ### Semantic Comparison Service
 

docs/user-guide/oa-get-started.md

@@ -49,7 +49,7 @@
    cd ../dine-in
    ```
 
-   This downloads and converts the Qwen2.5-VL-7B model (~7GB). This only needs to be done once.
+   This downloads and converts the MiniCPM-V-4.5 model to OpenVINO™ INT4. This only needs to be done once.
 
 3. **Prepare Test Data**
    - Add your food tray/plate images to the `images/` folder

docs/user-guide/oa-how-it-works.md

@@ -14,7 +14,7 @@ The Order Accuracy platform is an enterprise AI vision system designed for real-
 
 ### Key Features
 
-- **VLM-Powered Detection**: Uses Qwen2.5-VL-7B for accurate item identification
+- **VLM-Powered Detection**: Uses MiniCPM-V-4.5 (INT4) for accurate item identification
 - **Intel Hardware Optimization**: Optimized for Intel CPUs and GPUs via OpenVINO
 - **Dual Application Support**: Dine-In (image-based) and Take-Away (video stream-based)
 - **Semantic Matching**: Fuzzy matching for item name variations
@@ -44,7 +44,7 @@ graph TB
         end
 
         subgraph "Shared Services"
-            OVMS[OVMS VLM<br>Qwen2.5-VL-7B]
+            OVMS[OVMS VLM<br>MiniCPM-V-4.5]
             SEM[Semantic Service]
             MINIO[MinIO Storage]
         end
@@ -76,7 +76,7 @@ flowchart TB
         DVS --> DMET["Metrics Collector"]
     end
 
-    DVLM --> OVMS["OVMS VLM<br/>(Qwen2.5-VL)"]
+    DVLM --> OVMS["OVMS VLM<br/>(MiniCPM-V-4.5)"]
     DSEM --> SEM["Semantic Service"]
 ```
 
@@ -91,7 +91,7 @@ flowchart TB
     minio --> selector["Frame Selector<br/>(YOLO11n-CPU)"]
     selector -->|top 3 frames| scheduler["VLM Scheduler<br/>(ThreadPool)"]
     selector -->|top 3 frames| validation["Validation Agent"]
-    scheduler --> ovms["OVMS VLM<br/>(Qwen2.5-VL, GPU-INT8)"]
+    scheduler --> ovms["OVMS VLM<br/>(MiniCPM-V-4.5, GPU-INT4)"]
     validation --> semantic["Semantic Service"]
   end
 
@@ -105,12 +105,12 @@ flowchart TB
 
 #### 1. VLM Backend (OVMS)
 
-OpenVINO™ Model Server hosting Qwen2.5-VL-7B for vision-language inference.
+OpenVINO™ Model Server hosting MiniCPM-V-4.5 for vision-language inference.
 
 **Features:**
 
 - OpenAI-compatible API (`/v3/chat/completions`)
-- INT8 quantization for optimized performance
+- INT4 weight compression for optimized performance
 - GPU acceleration via Intel/NVIDIA hardware
 - Shared model instance for both applications
 
@@ -120,7 +120,7 @@ OpenVINO™ Model Server hosting Qwen2.5-VL-7B for vision-language inference.
 response = requests.post(
     f"{OVMS_ENDPOINT}/v3/chat/completions",
     json={
-        "model": "Qwen/Qwen2.5-VL-7B-Instruct",
+        "model": "openbmb/MiniCPM-V-4_5-int4",
         "messages": [
             {
                 "role": "user",
@@ -235,7 +235,7 @@ Request batching scheduler optimizing OVMS throughput.
      - Store selected frames in MinIO
 
 3. **VLM Processing**:
-   - VLM Scheduler → OVMS (Qwen2.5-VL):
+   - VLM Scheduler → OVMS (MiniCPM-V-4.5):
      - Batch frames by time window
      - Send to OVMS with detection prompt
      - Parse structured item response

docs/user-guide/take-away/get-started.md

@@ -82,7 +82,7 @@ cd ../take-away
 
 This downloads and exports:
 
-- Qwen2.5-VL-7B-Instruct (OpenVINO™ format)
+- MiniCPM-V-4_5 INT4 (OpenVINO™ format)
 - YOLOv11 model (INT8 OpenVINO™)
 - EasyOCR detection and recognition models
 
@@ -112,9 +112,16 @@ make up
 # =============================================================================
 VLM_BACKEND=ovms
 OVMS_ENDPOINT=http://ovms-vlm:8000
-OVMS_MODEL_NAME=Qwen/Qwen2.5-VL-7B-Instruct
+OVMS_MODEL_NAME=openbmb/MiniCPM-V-4_5-int4
+VLM_PRECISION=int4           # must match the -int4 suffix in OVMS_MODEL_NAME
 TARGET_DEVICE=GPU            # 'GPU' or 'CPU' — also set OPENVINO_DEVICE to match
 
+# =============================================================================
+# VLM Image / Decoding Options
+# =============================================================================
+VLM_IMAGE_MAX_SIZE=448       # frames are fitted into a 448x448 square canvas
+VLM_ENABLE_THINKING=false    # keep MiniCPM's <think> reasoning disabled
+
 # =============================================================================
 # Inference Device (must match TARGET_DEVICE)
 # =============================================================================
@@ -138,6 +145,15 @@ MINIO_ENDPOINT=minio:9000
 
 > **Changing the inference device:** Set both `TARGET_DEVICE` and `OPENVINO_DEVICE` to the same value (`GPU` or `CPU`), then re-run `./setup_models.sh --app take-away` to re-export the model for that device.
 
+> **`VLM_ENABLE_THINKING`:** MiniCPM-V-4.5 is a hybrid reasoning model. Leave this
+> `false`. When enabled, the model spends the whole token budget on a `<think>`
+> reasoning block and the detected-item list is truncated, which shows up as
+> missing items and ~4x higher latency.
+
+> **`VLM_IMAGE_MAX_SIZE`:** Each selected frame is fitted into a square canvas of
+> this size (aspect ratio preserved, white padding), matching the dine-in
+> pipeline. Larger values increase detail but also prompt tokens and latency.
+
 ### Validate Configuration
 
 ```bash

docs/user-guide/take-away/get-started/build-from-source.md

@@ -78,7 +78,7 @@ cd ../take-away
 
 This downloads and exports:
 
-- **Qwen2.5-VL-7B-Instruct** (OpenVINO™ INT8) → `ovms-service/models/`
+- **MiniCPM-V-4_5** (OpenVINO™ INT4) → `ovms-service/models/`
 - **EasyOCR** models → `take-away/models/easyocr/`
 - **YOLO11n** (FP32 + INT8 OpenVINO) → `take-away/models/`
 

docs/user-guide/take-away/get-started/system-requirements.md

@@ -24,7 +24,7 @@ Hardware, software, and network requirements for deploying Take-Away Order Accur
 | **GPU**     | Intel® Data Center GPU Max (48 GB) — for 4+ concurrent stations |
 | **Storage** | 200 GB NVMe SSD                                                 |
 
-**GPU VRAM guidance:** The Qwen2.5-VL-7B INT8 model requires ~8 GB of VRAM.
+**GPU VRAM guidance:** The MiniCPM-V-4_5 INT4 model requires ~5 GB of VRAM.
 The default `cache_size=4` reserves an additional 4 GB VRAM for the KV cache. Total VRAM needed
 is around 12 GB, which fits in an Intel® Arc™ A770 16 GB. On **integrated GPU** (iGPU)
 platforms such as Wildcat Lake and Meteor Lake, the KV cache is drawn from **system RAM**

docs/user-guide/take-away/how-it-works.md

@@ -15,7 +15,7 @@ flowchart TB
     minio --> selector["Frame Selector<br/>(YOLO11n-CPU)"]
     selector -->|top 3 frames| scheduler["VLM Scheduler<br/>(ThreadPool)"]
     selector -->|top 3 frames| validation["Validation Agent"]
-    scheduler --> ovms["OVMS VLM<br/>(Qwen2.5-VL, GPU-INT8)"]
+    scheduler --> ovms["OVMS VLM<br/>(MiniCPM-V-4.5, GPU-INT4)"]
     validation --> semantic["Semantic Service"]
   end
 
@@ -107,7 +107,7 @@ WORKERS=0
 │                        ▼                                                    │
 │  ┌──────────────────────────────────────────────────────────────────────┐   │
 │  │                           OVMS VLM (GPU)                             │   │
-│  │              Qwen2.5-VL-7B / Continuous Batching                     │   │
+│  │            MiniCPM-V-4.5 INT4 / Continuous Batching                  │   │
 │  └──────────────────────────────────────────────────────────────────────┘   │
 │                                                                             │
 │  Characteristics:                                                           │
@@ -317,7 +317,7 @@ response = requests.post(
      - Store selected frames in MinIO
 
 3. **VLM Processing**:
-   - VLM Scheduler → OVMS (Qwen2.5-VL):
+   - VLM Scheduler → OVMS (MiniCPM-V-4.5):
      - Batch frames by time window
      - Send to OVMS with detection prompt
      - Parse structured item response
@@ -387,20 +387,20 @@ rtspsrc location=<url> latency=0 buffer-mode=0 protocols=tcp ntp-sync=false do-r
 ┌─────────────────────────────────────────────────────────────────────────────────┐
 │                         OVMS VLM INTEGRATION                                     │
 │                                                                                  │
-│  Model: Qwen/Qwen2.5-VL-7B-Instruct                                             │
+│  Model: openbmb/MiniCPM-V-4_5-int4                                              │
 │                                                                                  │
 │  ┌────────────────────────────────────────────────────────────────────────┐    │
 │  │                         OVMS Model Server                               │    │
 │  │                                                                         │    │
 │  │  ┌────────────────┐    ┌────────────────┐    ┌────────────────┐       │    │
 │  │  │  Vision        │    │  Language      │    │  Output        │       │    │
 │  │  │  Encoder       │───▶│  Model         │───▶│  Decoder       │       │    │
-│  │  │  (ViT-based)   │    │  (Qwen2.5)     │    │  (JSON)        │       │    │
+│  │  │  (SigLIP)      │    │  (MiniCPM)     │    │  (JSON)        │       │    │
 │  │  └────────────────┘    └────────────────┘    └────────────────┘       │    │
 │  │                                                                         │    │
 │  │  API: OpenAI-compatible /v3/chat/completions                           │    │
 │  │  Port: 8001 (configurable)                                             │    │
-│  │  Precision: INT8 (optimized for inference)                             │    │
+│  │  Precision: INT4 (optimized for inference)                             │    │
 │  └────────────────────────────────────────────────────────────────────────┘    │
 │                                                                                  │
 └─────────────────────────────────────────────────────────────────────────────────┘
@@ -412,7 +412,7 @@ rtspsrc location=<url> latency=0 buffer-mode=0 protocols=tcp ntp-sync=false do-r
 
 ```json
 {
-  "model": "Qwen/Qwen2.5-VL-7B-Instruct",
+  "model": "openbmb/MiniCPM-V-4_5-int4",
   "messages": [
     {
       "role": "user",
@@ -426,10 +426,17 @@ rtspsrc location=<url> latency=0 buffer-mode=0 protocols=tcp ntp-sync=false do-r
     }
   ],
   "max_completion_tokens": 100,
-  "temperature": 0.2
+  "temperature": 0.2,
+  "chat_template_kwargs": { "enable_thinking": false }
 }
 ```
 
+> **`enable_thinking`:** MiniCPM-V-4.5 is a hybrid reasoning model. Unless
+> thinking is explicitly disabled, its chat template opens a `<think>` block and
+> the entire `max_completion_tokens` budget is spent on reasoning, leaving the
+> item list truncated. The service always sends `enable_thinking: false`
+> (configurable via `VLM_ENABLE_THINKING`).
+
 **Response:**
 
 ```json

docs/user-guide/take-away/release-notes.md

@@ -4,6 +4,36 @@ Version history and changelog for Take-Away Order Accuracy.
 
 ---
 
+## Version 2026.1.0 (Unreleased)
+
+### What's New
+
+- **VLM switched to MiniCPM-V-4.5 (INT4)** — Take-Away now uses
+  `openbmb/MiniCPM-V-4_5-int4` instead of `Qwen/Qwen2.5-VL-7B-Instruct`, aligning it
+  with Dine-In so both applications share a single OVMS model instance.
+  `VLM_PRECISION` defaults to `int4`.
+- **`setup_models.sh` per-app defaults** — `--app take-away` and `--app dine-in` both
+  default to MiniCPM-V-4.5 INT4. `VLM_PRECISION` is now derived from the precision
+  suffix of `OVMS_MODEL_NAME`, and a mismatch between the two emits a warning.
+- **Reasoning disabled by default** — MiniCPM-V-4.5 is a hybrid reasoning model; the
+  service now sends `chat_template_kwargs.enable_thinking=false` (configurable via
+  `VLM_ENABLE_THINKING`) so the token budget is spent on the answer instead of a
+  `<think>` block. Any stray `<think>` output is stripped before parsing.
+- **Aspect-preserving image preprocessing** — frames are fitted into a 448x448 square
+  canvas (LANCZOS downscale, white padding, light contrast/sharpening) matching the
+  Dine-In pipeline, configurable via `VLM_IMAGE_MAX_SIZE`. This replaces the previous
+  fixed 512x512 resize, which distorted the aspect ratio.
+
+### Fixed
+
+- Frames are no longer stretched to 512x512, which distorted product shapes.
+- Detections are no longer truncated by reasoning output, which previously caused
+  missing items and roughly 4x higher VLM latency.
+- `setup_models.sh` no longer migrates legacy Qwen weights into a directory named
+  after a different model.
+
+---
+
 ## Version 2026.0.0 (March 2026)
 
 **General Availability Release**

docs/user-guide/take-away/ta-benchmarking.md

@@ -62,6 +62,13 @@ make up
 > make download-sample-video
 > ```
 
+> **Order manifests:** The benchmark validates the orders detected in the video
+> against `config/orders.json`. Order IDs must match the order numbers shown in
+> the video, and each order's expected items must match what is actually visible.
+> A stale manifest reports correct detections as mismatches; an unparseable one
+> (for example a trailing comma) makes every order fail and the benchmark reports
+> zero transactions.
+
 ## Benchmark Commands
 
 ### Fixed Workers Benchmark