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| title: README | |
| emoji: ⚡ | |
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| https://cdn-uploads.huggingface.co/production/uploads/69e33f87d78d475613a7bd9a/l1H9DLocxXupi_GCaYEGM.png | |
| <div align="center"> | |
| <img width="700px" src="./OptAIxHuggingFace.png"> | |
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| # Welcome to Opt.Gear | |
| This is the official Hugging Face organization for **Opt.Gear**, the family of On-Device language models developed by **OptAI Inc.** | |
| We will continuously release and update **On-Device LLM, LMM and Small Agent Model** designed for edge AI applications. | |
| Build and scale yout AI services with models optimized specifically for On-Device deployment. | |
| ## Opt.Gear Family of Open-Weight Models | |
| - **Opt.Gear**: A family of large language models optimized for on-device inference, available in **1B** and **270M** parameter sizes. | |
| - **Opt.Gear-QAT**: Quantization-aware large language models designed for efficient deployment on on-device platforms. | |
| - **Opt.Gear-1M**: An ultra-lightweight language model designed for deployment on MCUs, including platforms such as FPGA and Raspberry Pi. | |
| - (TBU) **Opt.FGear**: A tool-calling language model designed for on-device AI agents. | |
| - (TBU) **Opt.VGear**: A lightweight vision-language model (VLM) for a wide range of multimodal tasks. | |
| - (TBU) **Opt.Gear-MTP**: A draft model for accelerating inference in on-device environments through multi-token prediction. |