🚀 Real-Time On-Device AI Agent with Polaris-4B — Run It Yourself, No Cloud, No Cost
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🚀 Real-Time On-Device AI Agent with Polaris-4B — Run It Yourself, No Cloud, No Cost
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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.
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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.
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📱 What’s remarkable?
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📱 What’s remarkable?
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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?
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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?
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➡️ It’s totally automated, free to use, and anyone can do it.
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➡️ It’s totally automated, free to use, and anyone can do it.
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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.
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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.
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🧠 About the Model
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🧠 About the Model
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- Model: Polaris-4B-Preview
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- Model: Polaris-4B-Preview
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- Size: ~4B parameters
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- Size: ~4B parameters
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- Ranking: Top 3 on Hugging Face LLM Leaderboard (<6B)
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- Ranking: Top 3 on Hugging Face LLM Leaderboard (<6B)
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- Tokenizer: Token-incremental inference supported
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- Tokenizer: Token-incremental inference supported
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- Modifications: None — stock weights, just optimized for mobile
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- Modifications: None — stock weights, just optimized for mobile
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⚙️ What ZETIC.MLange Does
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⚙️ What ZETIC.MLange Does
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 ZETIC.MLange is a fully automated deployment framework for On-Device AI, built for AI engineers who want to focus on models — not infrastructure.
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 ZETIC.MLange is a fully automated deployment framework for On-Device AI, built for AI engineers who want to focus on models — not infrastructure.
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Here’s what it does in minutes:
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Here’s what it does in minutes:
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- 📊 Analyzes model structure
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- 📊 Analyzes model structure
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- ⚙️ Converts to mobile-optimized format (e.g., GGUF, ONNX)
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- ⚙️ Converts to mobile-optimized format (e.g., GGUF, ONNX)
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- 📦 Generates a runnable runtime environment with pre/post-processing
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- 📦 Generates a runnable runtime environment with pre/post-processing
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- 📱 Targets real mobile hardware (CPU, GPU, NPU — including Qualcomm, MediaTek, Apple)
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- 📱 Targets real mobile hardware (CPU, GPU, NPU — including Qualcomm, MediaTek, Apple)
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- 🎯 Gives you a downloadable SDK or mobile app component — ready to run
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- 🎯 Gives you a downloadable SDK or mobile app component — ready to run
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And yes — this is available now, for free, at 
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And yes — this is available now, for free, at 
https://mlange.zetic.ai
https://mlange.zetic.ai
link
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🧪 For AI Engineers Like You, If you want to:
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🧪 For AI Engineers Like You, If you want to:
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- Test LLMs directly on-device
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- Test LLMs directly on-device
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- Run models offline with no latency
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- Run models offline with no latency
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- Avoid cloud GPU costs
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- Avoid cloud GPU costs
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- Deploy to mobile without writing app-side inference code
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- Deploy to mobile without writing app-side inference code
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Then this is your moment. You can do exactly what we did, using your own models — all in a few clicks.
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Then this is your moment. You can do exactly what we did, using your own models — all in a few clicks.
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🎯 Start here → 
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🎯 Start here → 
https://mlange.zetic.ai
https://mlange.zetic.ai
link
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📬 Want to try Polaris-4B on your own app? contact@zetic.ai, or just visit 
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📬 Want to try Polaris-4B on your own app? contact@zetic.ai, or just visit 
https://mlange.zetic.ai
https://mlange.zetic.ai
link
 , it is opened as free!
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 , it is opened as free!
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Great work 
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Great work 
@Chancy
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Chancy
, 
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, 
@Zhihui
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Zhihui
 , 
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 , 
@tobiaslee
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tobiaslee
 !
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 !
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🚀 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? contact@zetic.ai, or just visit https://mlange.zetic.ai , it is opened as free!

Great work @Chancy, @Zhihui , @tobiaslee !

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