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MiniCPM5-2B_wi8_afp32.litertlm

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README.md

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 # MiniCPM5-2B (LiteRT-LM)
 
-This repository hosts the [**LiteRT-LM**](https://ai.google.dev/edge/litert-lm) (LiteRT formerly known as TensorFlow Lite) version of **MiniCPM5-2B**, optimized for fully on-device inference on mobile and edge hardware.
+This repository hosts a [**LiteRT-LM**](https://ai.google.dev/edge/litert-lm) (LiteRT, formerly TensorFlow Lite) conversion of **[openbmb/MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B)**, optimized for fully on-device inference on mobile and edge hardware.
 
----
-
-## Available Models
-
-* **`minicpm_wi4c_wi8_afp32.litertlm`**: This model features mixed INT4/INT8 weight-only quantization with FP32 activations (afp32). MLP projections use channelwise INT4 with Hadamard rotation; all remaining weights (attention, embedding, and lmhead) use channelwise INT8.
-* **`minicpm_wi8_afp32.litertlm`**: This model features weight-only INT8 quantization (wi8) with FP32 activations (afp32).
-
-## What is MiniCPM?
+## Available Files
 
-**MiniCPM5-2B** is part of the **MiniCPM5** series from [OpenBMB](https://huggingface.co/openbmb). It is a dense **2.5B-parameter** Transformer built specifically for **on-device, local, and resource-constrained deployment**.
+* **`MiniCPM5-2B_wi8_afp32.litertlm`**: dynamic weight-only INT8 quantization (`wi8`) with FP32 activations (`afp32`). KV cache buffer length 1280 tokens.
 
-### Highlights
+## Conversion Details
 
-- 🧠 **Hybrid Reasoning** — a single checkpoint serves as both a fast assistant and a deliberate reasoner via a built-in `<think>` template (`enable_thinking`).
-- 🛠️ **Tool use** — native tool-calling support in the chat template.
-- 📏 **Long context** — native **131,072**-token context length.
-- 📱 **Built for the edge** — compact footprint designed for local assistants, coding agents, and tool-use workflows.
+The model was re-authored with `ai-edge-torch` (Llama-architecture decoder-only builder, since MiniCPM5-2B ships as a standard `LlamaForCausalLM`), verified for numerical parity against the original HF checkpoint (cosine similarity 0.999995 on last-token logits), converted to a multi-signature (`prefill_8/64/128/256/512/1024` + `decode`) `.tflite` with INT8 dynamic weight-only quantization, and packaged into `.litertlm` with `litert-lm-builder`.
 
-### Model Information
+## Model Information
 
 | Item | Value |
 | --- | --- |
 | Type | Causal Language Model |
 | Architecture | Standard `LlamaForCausalLM` |
-| Parameters | 2,516,756,480 (~2.5B) |
-| Non-Embedding Parameters | 1,981,982,720 |
 | Layers | 42 |
-| Attention Heads (GQA) | 16 (Q) / 2 (KV), head dim 128 |
-| Hidden / Intermediate Size | 2048 / 6144 |
-| Vocabulary Size | 130,560 |
-| Context Length | 131,072 |
-
----
-
-## Use the model
+| Hidden size | 2048 |
+| Attention Heads (GQA) | 16 (Q) / 2 (KV), head_dim 128 |
+| Vocab size | 130,560 |
+| Context Length (native) | 131,072 |
+| Exported KV cache length | 1,280 |
 
-### Edge Gallery App (Android)
-
-1. **Get the App**: Install the [app](https://play.google.com/store/apps/details?id=com.google.ai.edge.gallery&pli=1) from Google Play or download the latest APK from the [GitHub releases page](https://github.com/google-ai-edge/gallery/releases).
-2. **Importing the Model**: Navigate to the **Model manager** within the app and click the **"+" (plus)** icon in the bottom-right corner. Two options will appear:
-   * **Import from HF (Recommended)**: Select this option, and a dialog box will appear showing an example Hugging Face model URL. Enter the HF link for the desired `.litertlm` model and click submit. The model will then appear in your list, and you can proceed to download it (a Hugging Face account login is required).
-   * **From local model file**: First, download the `.litertlm` model directly to your Android device, OR download it to your computer and push it via ADB (e.g., `adb push minicpm_wi4c_wi8_afp32.litertlm /sdcard/Download/`). Then, select this option, choose the downloaded file from your storage, configure your preferred parameters, and tap **"Import"**.
+## Verification
 
-For full details on importing models and other features, see the [Edge Gallery App Wiki](https://github.com/google-ai-edge/gallery/wiki).
+- **Numerical parity**: re-authored `ai-edge-torch` model vs. original HF `transformers` forward pass — cosine similarity **0.999995** on last-token logits (identical top-5 token predictions).
+- **End-to-end generation**: verified with `litert-lm run`, including correct `<think>...</think>` reasoning-channel parsing and coherent completions (factual QA + code generation tested).
 
-To build the demo app from source, please follow the [instructions](https://github.com/google-ai-edge/gallery/blob/main/README.md) from the GitHub repository.
+## Use the model
 
-### Try It (Desktop/CLI)
+### Desktop/CLI
 
 Install `uv` and run the model directly from the LiteRT-LM command line:
 
 ```bash
 uv tool install litert-lm
-uvx litert-lm run --from-huggingface-repo=litert-community/MiniCPM5-2B minicpm_wi4c_wi8_afp32.litertlm --prompt="What is the capital of France?"
+uvx litert-lm run --from-huggingface-repo=litert-community/MiniCPM5-2B MiniCPM5-2B_wi8_afp32.litertlm --prompt="What is the capital of France?"
 ```
 
+### Edge Gallery App (Android)
+
+1. Install the [app](https://play.google.com/store/apps/details?id=com.google.ai.edge.gallery) from Google Play or the [GitHub releases page](https://github.com/google-ai-edge/gallery/releases).
+2. In **Model manager**, tap **+ → Import from HF**, paste this repo's `.litertlm` URL, and download.
+
 ## Links
 
 - 🤗 Original model (BF16): [openbmb/MiniCPM5-2B](https://huggingface.co/openbmb/MiniCPM5-2B)
-- 🤗 Smaller sibling: [litert-community/MiniCPM5-1B](https://huggingface.co/litert-community/MiniCPM5-1B)
 - 📦 GitHub: [OpenBMB/MiniCPM](https://github.com/OpenBMB/MiniCPM)
 - 🛠️ LiteRT docs: [ai.google.dev/edge/litert](https://ai.google.dev/edge/litert)
-
----
+- Reference conversion for the smaller checkpoint: [litert-community/MiniCPM5-1B](https://huggingface.co/litert-community/MiniCPM5-1B)
 
 ## License