Instructions to use Slinkies86/e2b_multimodal_agent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use Slinkies86/e2b_multimodal_agent with LiteRT:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
| license: other | |
| language: | |
| - en | |
| tags: | |
| - android | |
| - edge-ai | |
| - litert | |
| - multimodal | |
| - on-device | |
| - anyone-hub | |
| - tflite | |
| pipeline_tag: text-generation | |
| # ⚡ anyone-Hub: E2B Multimodal Agent | |
| The **Anyone-Hub E2B Multimodal Agent** is a bleeding-edge, 2-billion parameter multimodal Large Language Model engineered strictly for hyper-optimized, native Android deployment. | |
| Compiled into the proprietary `.litertlm` payload format, this model acts as the real-time observation and execution brain for the Anyone-Hub developer ecosystem, capable of streaming directly into a 16KB-aligned local Android NPU/GPU execution bridge. | |
| ## 🎯 Why This Was Built | |
| Legacy approaches to running local AI on Android rely on bloated Python wrappers, Termux-style bootstraps, or cross-compiled architectures that fail modern Play Store integrity checks. | |
| The E2B agent was built from the ground up to solve the "Mobile AI Sandbox" problem. It is designed to sit safely on the Android host side while securely monitoring standard I/O streams from an isolated, pKVM/AVF (Android Virtualization Framework) Debian rootfs. This guarantees zero-latency terminal monitoring and command execution without compromising the hardware-level security sandbox. | |
| ## 🛠️ What It Is For | |
| This model serves as the rapid-response agentic core for the Anyone-IDE and terminal matrix. It features: | |
| * **Real-Time Terminal Orchestration:** Passively monitors high-speed Cap'n Proto I/O streams and injects precise CLI commands. | |
| * **Hardware-Accelerated Multimodality:** Packed with dedicated `.tflite` adapters for Vision encoding and Audio processing directly on the device. | |
| * **Speculative Decoding (MTP):** Features an advanced Multi-Token Prediction (MTP) drafter to massively accelerate token generation speeds on mobile hardware. | |
| ## ⚠️ Architectural Warning | |
| **DO NOT attempt to load this model using `transformers`, `llama.cpp`, or standard PyTorch pipelines.** This `.litertlm` file is structurally incompatible with traditional LLM loaders. It is a highly compressed, uncompressed-ZIP analog containing TFLite model sections and a SentencePiece vocabulary, engineered exclusively to be fetched dynamically to local device storage (`Context.getFilesDir()`) and executed via the Anyone-Hub Android LiteRT-LM C++ JNI bridge (`liblitertlm_jni.so`). | |
| ## 📥 App Implementation (Direct Download) | |
| For internal AnyOne-Hub Kotlin implementation, use the raw resolution link to bypass the Hugging Face UI and stream directly into the local file system: | |
| ```text | |
| [https://huggingface.co/Slinkies86/e2b_multimodal_agent/resolve/main/e2b_multimodal_agent.litertlm](https://huggingface.co/Slinkies86/e2b_multimodal_agent/resolve/main/e2b_multimodal_agent.litertlm) | |
| Copyright © 2024 anyone-Hub |