Tag

#on-device

Every item tagged on-device, newest first.

4 items

Welcome Gemma 4: Frontier multimodal intelligence on device

Google introduced Gemma 4, a multimodal model capable of processing text, images, and audio on-device. Gemma 4 enables developers to build applications with frontier intelligence. You can deploy Gemma 4 on Android and iOS devices.

Key takeaways

Gemma 4 supports multimodal input including text, images, and audio.
On-device deployment is possible on Android and iOS.
Developers can access Gemma 4 for building applications.

HHugging Face Blog#multimodal #on-device #frontier-models

researchMar 5

Bringing Robotics AI to Embedded Platforms: Dataset Recording, VLA Fine‑Tuning, and On‑Device Optimizations

Researchers from NXP and Hugging Face collaborated on bringing robotics AI to embedded platforms. They developed methods for dataset recording, fine-tuning vision-language-action models, and on-device optimizations. This enables running AI models on resource-constrained embedded systems, expanding AI deployment options for builders. The approach allows for efficient AI model execution on devices with limited resources.

Key takeaways

Enables AI on resource-constrained embedded systems.
Developed methods for dataset recording and VLA fine-tuning.
On-device optimizations improve model efficiency.

HHugging Face Blog#embedded-ai #robotics #fine-tuning #on-device

modelsSep 25

Llama can now see and run on your device - welcome Llama 3.2

Meta released Llama 3.1, an update to the Llama model family that adds on-device execution capabilities. The model can run locally on devices with sufficient RAM, expanding deployment options for builders. Local execution enables lower latency and no reliance on cloud infrastructure. This update targets applications requiring real-time responses or offline functionality.

Key takeaways

Llama 3.1 supports on-device execution on devices with sufficient RAM.
Enables lower latency and offline functionality.
Expands deployment options for local and edge applications.

HHugging Face Blog#local-llm #on-device #edge-ai

modelsMar 20

A Chatbot on your Laptop: Phi-2 on Intel Meteor Lake

Microsoft researchers ran Phi-2, a 2.7B parameter LLM, on an Intel Meteor Lake laptop with on-device stable diffusion. The demo shows feasible deployment of small LLMs on consumer hardware. You can run similar benchmarks with Phi-2 on your own hardware using the Hugging Face model hub.

Key takeaways

Phi-2 runs on Intel Meteor Lake with on-device stable diffusion.
2.7B parameter LLM feasible on consumer hardware.
Use Hugging Face model hub for similar benchmarks.

HHugging Face Blog#local-llm #on-device #benchmarks