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README.md

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-license: apache-2.0
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+# MiniCPM-V-4 · LiteRT INT8 (on-device, Snapdragon 8850 CPU)
+
+`MiniCPM-V-4-int8.litertlm` — an on-device [LiteRT-LM](https://github.com/google-ai-edge/LiteRT-LM) build of MiniCPM-V-4, quantized to **INT8** (weight-only, GPTQ + Hadamard rotation) and packaged as a single `.litertlm` bundle for CPU inference on **Snapdragon 8850**.
+
+## Model
+
+For the base model, architecture, capabilities and license, refer to the original model card:
+
+**https://huggingface.co/openbmb/MiniCPM-V-4**
+
+This repository only provides an **edge-optimized LiteRT deployment** of that model:
+
+- **Format**: `.litertlm` (LiteRT-LM bundle: tokenizer + LLM prefill/decode + embedder + navit SigLIP vision encoder + resampler).
+- **Quantization**: INT8 weight-only, per-channel, GPTQ + Hadamard rotation (activation-outlier smoothing).
+- **Vision**: official multi-slice preprocessing (thumbnail + sub-tiles, 64 tokens per slice, up to 9 slices), navit SigLIP.
+
+## Performance (Snapdragon 8850, CPU backend)
+
+Measured on-device (arm64 CPU, `--backend=cpu`), single image, 4-slice input:
+
+| Metric | Value |
+|---|---|
+| Prefill speed | **40.9 tokens/sec** |
+| Decode speed | **16.6 tokens/sec** |
+| Vision encode (4 slices) | **~2.04 s** |
+| Time to first token | ~7.0 s (284-token prefill incl. vision) |
+| Init (model load + compile) | ~2.2 s |
+
+## Accuracy — MME benchmark (on-device, 8850 CPU)
+
+Full MME evaluation running the INT8 bundle on-device:
+
+| Group | Score |
+|---|---|
+| **Perception** | **1568** |
+| **Cognition** | **491** |
+| **Total** | **2059** |
+
+## Usage
+
+Run with a LiteRT-LM CPU runner:
+
+```bash
+LD_LIBRARY_PATH=. ./litert_lm_main \
+  --model_path=./MiniCPM-V-4-int8.litertlm \
+  --backend=cpu \
+  --image_path=./image.jpg \
+  --input_prompt="What is in this image?"
+```
+
+## License
+
+Follows the license of the base model — see **https://huggingface.co/openbmb/MiniCPM-V-4**.