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7 days ago
happyme531/Florence-2-base-ft-ONNX-RKNN2
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10 days ago
🚀 Real-Time On-Device AI Agent with Polaris-4B — Run It Yourself, No Cloud, No Cost
We just deployed a real-time on-device AI agent using the Polaris-4B-Preview model — one of the top-performing <6B open LLMs on Hugging Face.
📱 What’s remarkable?
This model runs entirely on a mobile device, without cloud, and without any manual optimization. It was built using ZETIC.MLange, and the best part?
➡️ It’s totally automated, free to use, and anyone can do it.
You don’t need to write deployment code, tweak backends, or touch device-specific SDKs. Just upload your model — and ZETIC.MLange handles the rest.
🧠 About the Model
- Model: Polaris-4B-Preview
- Size: ~4B parameters
- Ranking: Top 3 on Hugging Face LLM Leaderboard (<6B)
- Tokenizer: Token-incremental inference supported
- Modifications: None — stock weights, just optimized for mobile
⚙️ What ZETIC.MLange Does
ZETIC.MLange is a fully automated deployment framework for On-Device AI, built for AI engineers who want to focus on models — not infrastructure.
Here’s what it does in minutes:
- 📊 Analyzes model structure
- ⚙️ Converts to mobile-optimized format (e.g., GGUF, ONNX)
- 📦 Generates a runnable runtime environment with pre/post-processing
- 📱 Targets real mobile hardware (CPU, GPU, NPU — including Qualcomm, MediaTek, Apple)
- 🎯 Gives you a downloadable SDK or mobile app component — ready to run
And yes — this is available now, for free, at https://mlange.zetic.ai
🧪 For AI Engineers Like You, If you want to:
- Test LLMs directly on-device
- Run models offline with no latency
- Avoid cloud GPU costs
- Deploy to mobile without writing app-side inference code
Then this is your moment. You can do exactly what we did, using your own models — all in a few clicks.
🎯 Start here → https://mlange.zetic.ai
📬 Want to try Polaris-4B on your own app? [email protected], or just visit https://mlange.zetic.ai , it is opened as free!
Great work @Chancy, @Zhihui , @tobiaslee !
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12 days ago
💫 Next-Level On-Device AI Showdown
🪽 See It to Believe It, How QWEN4b works at On-device environment without expensive GPU Cloud server?
We’ve crafted a side-by-side demo video showcasing both Jan-Nano and QWEN 4B in action—no more wondering which model reigns supreme. Click play, compare their speeds, accuracy, and memory footprints, and decide which one fits your needs best!
👋 Why You Can’t Miss This
We are actively creating runnable sLLM environments for On-device AI. You can just build On-device AI apps within few hours.
Including Jan-Nano, QWEN4b, there are several sLLM models ready to be used on your AI application!.
🤑 Please feel free to use, because it is free to use!.
Ready to Compare?
Watch now, draw your own conclusions, and let us know which model you’d deploy in your next edge-AI project! 🌍💡
#OnDeviceAI #EdgeAI #AIShowdown #MLOptimization #DemoVideo #AIComparison
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