1. Download the model

A pre-quantized AWQ / GPTQ / AutoRound build is recommended.

git lfs install
git clone https://huggingface.co/Intel/Qwen3.5-2B-int4-AutoRound

2. Compile to bmodel

LLM compilation falls into two scenarios, controlled by --use_history_kv.

Without history KV — compiles two instruction groups: block_ (prefill) and block_cache_ (decode). Recommended for single-turn conversations with short context (e.g. within 4K):

# -s sets the max total sequence length;
# --max_input_length sets the max input length (defaults to -s if omitted).
llm_convert.py \
  -m /workspace/Qwen3.5/Qwen3.5-2B-int4-AutoRound \
  -s 2048 \
  --max_input_length 1024 \
  -c bm1684x \
  -o qwen3.5_4b

With history KV — compiles three instruction groups: block_ (prefill), block_kv_ (prefill with history), and block_cache_ (decode). Recommended for multi-turn conversations, long contexts (e.g. 8K), or when unsure — it is more flexible with good overall performance:

llm_convert.py \
  -m /workspace/Qwen3.5/Qwen3.5-2B-int4-AutoRound \
  -s 8192 \
  --use_history_kv \
  --chunk_length 1024 \
  -c bm1684x \
  -o qwen3.5_4b_history

--chunk_length sets the segment length for chunked inference: with 1K chunks, a 7K input runs prefill as block_$ + 7 \times $block_kv_; decode is also segmented by KV-cache length, so performance differs at 1K / 2K / 4K / 8K lengths.

Main arguments of llm_convert.py:

3. Run on PCIe / SoC

Copy the cpp_demo folder onto your device and build it:

mkdir build && cd build
cmake ..
make
mv pipeline ..
cd ..

Then run the bmodel:

./pipeline -m xxxx.bmodel -c config

Sample output:

The demo accepts slash commands (e.g. /exit, /clear) and uses @ to attach files — what is the image about? @./test.jpg for images, or what is it talking about? @./story.txt for text files (.txt / .md).

1. Prepare the working directory

mkdir model_yolov5s && cd model_yolov5s
cp ${REGRESSION_PATH}/model/yolov5s.onnx .
cp -rf ${REGRESSION_PATH}/dataset/COCO2017 .
cp -rf ${REGRESSION_PATH}/image .
mkdir workspace && cd workspace

2. Convert the model to MLIR

If the model takes images as input, the preprocessing must be specified. The preprocessing formula is:

$y = (x - \text{mean}) \times \text{scale}$

YOLOv5's official input is RGB scaled by 1/255, so mean = 0,0,0 and scale = 0.0039216,0.0039216,0.0039216.

model_transform.py \
  --model_name yolov5s \
  --model_def ../yolov5s.onnx \
  --input_shapes [[1,3,640,640]] \
  --mean 0.0,0.0,0.0 \
  --scale 0.0039216,0.0039216,0.0039216 \
  --keep_aspect_ratio \
  --pixel_format rgb \
  --output_names 350,498,646 \
  --test_input ../image/dog.jpg \
  --test_result yolov5s_top_outputs.npz \
  --mlir yolov5s.mlir

Main arguments of model_transform.py:

A ${model_name}_in_f32.npz file containing the preprocessed input is generated after this step.

3. MLIR → F16 bmodel

model_deploy.py \
  --mlir yolov5s.mlir \
  --quantize F16 \
  --processor bm1684x \
  --test_input yolov5s_in_f32.npz \
  --test_reference yolov5s_top_outputs.npz \
  --model yolov5s_1684x_f16.bmodel

Main arguments of model_deploy.py:

4. MLIR → INT8 bmodel

Run calibration first (typically 100–1000 images). Prefer symmetric quantization unless accuracy demands asymmetric.

run_calibration.py yolov5s.mlir \
  --dataset ../COCO2017 \
  --input_num 100 \
  -o yolov5s_cali_table

model_deploy.py \
  --mlir yolov5s.mlir \
  --quantize INT8 \
  --calibration_table yolov5s_cali_table \
  --processor bm1684x \
  --test_input yolov5s_in_f32.npz \
  --test_reference yolov5s_top_outputs.npz \
  --tolerance 0.85,0.45 \
  --model yolov5s_1684x_int8.bmodel

5. Verify the results

The sample script lives at python/samples/detect_yolov5.py.

# ONNX
detect_yolov5.py --input ../image/dog.jpg --model ../yolov5s.onnx          --output dog_origin.jpg
# F16 bmodel
detect_yolov5.py --input ../image/dog.jpg --model yolov5s_1684x_f16.bmodel --output dog_f16.jpg
# INT8 bmodel
detect_yolov5.py --input ../image/dog.jpg --model yolov5s_1684x_int8.bmodel --output dog_int8.jpg

Comparison of outputs: