| --- |
| license: apache-2.0 |
| language: |
| - en |
| - zh |
| base_model: |
| - openbmb/MiniCPM5-1B |
| pipeline_tag: text-generation |
| library_name: litert |
| tags: |
| - minicpm |
| - minicpm5 |
| - litert |
| - tflite |
| - on-device |
| - edge-ai |
| --- |
| |
| # MiniCPM5-1B (LiteRT-LM) |
|
|
| This repository hosts the [**LiteRT-LM**](https://ai.google.dev/edge/litert-lm) (LiteRT formerly known as TensorFlow Lite) version of **MiniCPM5-1B**, optimized for fully on-device inference on mobile and edge hardware. |
|
|
| --- |
|
|
| ## Available Models |
|
|
| * **`minicpm_dynamic_wi8_afp32_gpu_opt.litertlm`**: This model features dynamic weight-only INT8 quantization (wi8) with FP32 activations (afp32), heavily optimized for GPU execution. |
| |
| ## What is MiniCPM? |
| |
| **MiniCPM5-1B** is the first model in the **MiniCPM5** series from [OpenBMB](https://huggingface.co/openbmb). It is a dense **1B-parameter** Transformer built specifically for **on-device, local, and resource-constrained deployment**, while reaching **1B-class open-source SOTA** in its size class. |
| |
| ### Highlights |
| |
| - 🏆 **1B-class open-source SOTA** — strongest in tool use, code generation, and difficult reasoning among comparable open models. |
| - 🧠 **Hybrid Reasoning** — a single checkpoint serves as both a fast assistant and a deliberate reasoner via a built-in `<think>` template (`enable_thinking`). |
| - 📏 **Long context** — native **131,072**-token context length. |
| - 📱 **Built for the edge** — compact footprint designed for local assistants, coding agents, and tool-use workflows. |
|
|
| ### Model Information |
|
|
| | Item | Value | |
| | --- | --- | |
| | Type | Causal Language Model | |
| | Architecture | Standard `LlamaForCausalLM` | |
| | Parameters | 1,080,632,832 (~1B) | |
| | Non-Embedding Parameters | 679,552,512 | |
| | Layers | 24 | |
| | Attention Heads (GQA) | 16 (Q) / 2 (KV) | |
| | Context Length | 131,072 | |
|
|
| --- |
|
|
| ## Use the model |
|
|
| ### 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_dynamic_wi8_afp32_gpu_opt.litertlm /sdcard/Download/`). Then, select this option, choose the downloaded file from your storage, configure your preferred parameters, and tap **"Import"**. |
|
|
| For full details on importing models and other features, see the [Edge Gallery App Wiki](https://github.com/google-ai-edge/gallery/wiki). |
|
|
| 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. |
|
|
| ### Try It (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/MiniCPM5-1B minicpm_dynamic_wi8_afp32_gpu_opt.litertlm --prompt="What is the capital of France?" |
| ``` |
|
|
| ## Links |
|
|
| - 🤗 Original model (BF16): [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B) |
| - 📦 GitHub: [OpenBMB/MiniCPM](https://github.com/OpenBMB/MiniCPM) |
| - 🛠️ LiteRT docs: [ai.google.dev/edge/litert](https://ai.google.dev/edge/litert) |
|
|
| --- |
|
|
| ## License |
|
|
| Released under the **Apache-2.0 License**, consistent with the upstream [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B). |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{minicpm4, |
| title={MiniCPM4: Ultra-efficient LLMs on end devices}, |
| author={MiniCPM, Team}, |
| journal={arXiv preprint arXiv:2506.07900}, |
| year={2025} |
| } |
| ``` |
|
|
|
|
