The Phi Cookbook is a collection of hands-on resources and practical examples designed to help developers work with Microsoft's Phi series of small language models (SLMs). Unlike massive, resource-heavy generative AI models, these lightweight models are optimized for efficiency, making them suitable for deployment on laptops, mobile phones, or edge devices. The repository offers a structured learning path through various scenarios including text generation, coding, reasoning, and even audio/image applications, supporting multiple languages to ensure global accessibility.
- Supports GitHub Codespaces and Dev Containers for easy environment setup without local dependency issues.
- Capable of performing multi-language tasks across a wide range of regional variants like Arabic, Chinese, and Hindi.
- Enables offline and privacy-sensitive AI applications through edge deployment capabilities.
- Includes access to a Microsoft AI Discord community for developer support and collaboration.
- Supports GitHub Codespaces and Dev Containers for easy environment setup without local dependency issues.
- Capable of performing multi-language tasks across a wide range of regional variants like Arabic, Chinese, and Hindi.
- Enables offline and privacy-sensitive AI applications through edge deployment capabilities.
- Includes access to a Microsoft AI Discord community for developer support and collaboration.
Needle is an experimental 26m parameter Simple Attention Network distilled from Gemini 3.1, designed to redefine tiny AI for consumer devices like phones, watches, and glasses. It specializes in single-shot function calling, outperforming larger models such as FunctionGemma-270m and Qwen-0.6B in that specific domain. The project provides fully open weights, dataset generation methods, and tools for local finetuning on consumer hardware.
* 26m parameter architecture optimized for extremely small devices.
* High performance prefill and decode speeds when running on Cactus.
* Pretrained on 200B tokens and post-trained for function calling.
* Support for local finetuning via a web UI playground or CLI.
* 26m parameter architecture optimized for extremely small devices.
* High performance prefill and decode speeds when running on Cactus.
* Pretrained on 200B tokens and post-trained for function calling.
* Support for local finetuning via a web UI playground or CLI.
The article introduces the concept of Federated Language Models, combining edge-based Small Language Models (SLMs) with cloud-based Large Language Models (LLMs) for enhanced privacy and performance in AI applications.
Explore the top small language models of 2024, including Llama 3, Phi 3, Mixtral 8x7B, Gemma, and OpenELM. Learn about their features, benefits, and significance in the AI landscape.
