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
Upload 2 files
Browse filesThe Anyone-Hub E2B Multimodal Agent is a highly optimized, 2-billion parameter multimodal LLM engineered exclusively for native Android deployment. Packaged in the proprietary .litertlm format, this model acts as the real-time observation and execution brain for the Anyone-Hub developer platform. It features an advanced Multi-Token Prediction (MTP) drafter for ultra-fast speculative decoding, alongside dedicated hardware-accelerated audio and vision adapters. Designed to run completely on-device via a custom 16KB-aligned LiteRT-LM C++ engine, the E2B agent provides zero-latency terminal monitoring and command execution without compromising the Android Virtualization Framework (AVF) sandbox.
Key Features:
Format: .litertlm (Bundled TFLite adapter sections + SentencePiece Tokenizer)
Capabilities: Text, Vision, and Hardware-Accelerated Audio Processing.
Performance: Integrated MTP Drafter for Speculative Decoding.
Deployment: Optimized for Android NPU/GPU/OpenCL via liblitertlm_jni.so.
Copyright © 2024 anyone-Hub
- .gitattributes +1 -0
- e2b_multimodal_agent.litertlm +3 -0
- gemma4_e2b_agent.litertlm.json +52 -0
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