Add model

README.md

@@ -14,8 +14,6 @@ tags:
 
 # ConvMixer-768/32 (ONNX) – Renesas X5H
 
-> ⏳ **Model file not yet uploaded.** Benchmark results on this page were published ahead of the model weights — see **Provided Artifacts** below. Download/deployment steps will not work until the file is added to this repository.
-
 ## Introduction
 
 This repository hosts **ConvMixer-768/32**, targeting the **Renesas R-Car X5H** platform for
@@ -47,7 +45,7 @@ convmixer_768_32_..._optimized.onnx (FP32)
 
 | Artifact | Status | Notes |
 |----------|--------|-------|
-| **FP32 (ONNX)** | ⏳ Not yet uploaded | Benchmark numbers below exist; the model file has not been published to this repo yet |
+| **FP32 (ONNX)** | ✅ Published | `fp32/convmixer_768_32.onnx` — auto-cast to INT8 by the MWMX toolchain at compile time (see Deployment Flow above); no separate INT8 file is shipped |
 
 ## Performance
 
@@ -87,11 +85,13 @@ To run inference on Renesas R-Car X5H, you need:
 
 1. **Renesas R-Car X5H board** with NPX6 NPU
 2. **Renesas MWMX Runtime**
-3. **Hugging Face CLI** to download the model (once the model file is published)
+3. **Hugging Face CLI** to download the model
 
 ## Download
 
-TBD — model file not yet published to this repository.
+```bash
+hf download Renesas/ConvMixer-768-32-ONNX --repo-type=model --include "fp32/*"
+```
 
 ---
 

fp32/.metadata.yaml

@@ -0,0 +1,5 @@
+variant:
+  id: onnx_fp32
+  format: onnx
+  precision: fp32
+  method: onnx_export        # exported straight from the timm checkpoint, no quantization

fp32/convmixer_768_32.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:62f01e3a7caf4e1c57f3d5436aa2dd59d84eea01e61e7aaebe28afd4af0d28ff
+size 84953028

int8/benchmarks/x5h_mwmx_npu_apm50_1core.yaml

@@ -40,3 +40,25 @@ memory:
 
 power:
   avg_w: null
+
+# Exact commands verified against the NNAC "Getting Started" chapter. Rendered
+# by the AI-Dashboard in place of the generic placeholder flow — see
+# downloadRunHTML() / parse_reproduce() in AI-Dashboard/app.js.
+reproduce:
+  steps:
+    - title: Download the ONNX model
+      command: hf download Renesas/ConvMixer-768-32-ONNX --repo-type=model --include "fp32/*"
+    - title: Compile with the NNAC toolchain (INT8 auto-cast from the FP32 graph)
+      command: |
+        python3 nnac_frontend/legalize.py -d binary/nnx ./fp32/convmixer_768_32.onnx
+    - title: Set up the R-Car X5H board
+      command: Configure the board per the AI Compiler (NNAC) "Getting Started" guide, section 3.4 (host TFTP/NFS setup, bootloader flashing, U-Boot, Linux boot, login) -- exact steps depend on your board/network setup.
+    - title: Copy the compiled artifact to the board
+      command: Copy ${WORKDIR}/binary/nnx to the board -- method may vary (NFS mount, scp, USB, etc.).
+    - title: Run on R-Car X5H (single NPU cluster)
+      command: |
+        cd binary
+        ./host_app ./arc_prog_npus ./nnx/convmixer_768_32
+  notes: >-
+    hash[n] = 0x...(OK) means the run's output matches the reference hash in
+    hash.txt; latency is the NPX execution time reported in cycles and ms.