This tutorial provides a comprehensive coding walkthrough for building an advanced AI pipeline using Microsoft's Phi-4-mini language model. The guide demonstrates how to leverage this compact model for high-performance tasks within resource-constrained environments like Google Colab.
Key topics covered include:
- Setting up 4-bit quantized inference to optimize GPU memory usage.
- Implementing streaming chat and multi-step chain-of-thought reasoning.
- Executing native tool calling and function calling for agentic interactions.
- Building a retrieval-augmented generation (RAG) pipeline using FAISS and sentence transformers.
- Performing lightweight LoRA fine-tuning to inject new knowledge into the model.
Key topics covered include:
- Setting up 4-bit quantized inference to optimize GPU memory usage.
- Implementing streaming chat and multi-step chain-of-thought reasoning.
- Executing native tool calling and function calling for agentic interactions.
- Building a retrieval-augmented generation (RAG) pipeline using FAISS and sentence transformers.
- Performing lightweight LoRA fine-tuning to inject new knowledge into the model.
This article details research into finding the optimal architecture for small language models (70M parameters), exploring depth-width tradeoffs, comparing different architectures, and introducing Dhara-70M, a diffusion model offering 3.8x faster throughput with improved factuality.
The article presents rStar-Math, a method demonstrating that small language models (SLMs) can rival or surpass the math reasoning capabilities of larger models like OpenAI's without distillation. rStar-Math employs Monte Carlo Tree Search (MCTS) for 'deep thinking', using a math policy SLM guided by an SLM-based process reward model. It introduces three innovations: a code-augmented CoT data synthesis method for training the policy SLM, a novel process reward model training method avoiding step-level score annotation, and a self-evolution recipe where both the policy SLM and process preference model are iteratively improved. Through self-evolution with millions of solutions for 747k math problems, rStar-Math achieves state-of-the-art math reasoning, significantly improving performance on benchmarks like MATH and AIME.
The article discusses small language models (SLMs) designed for high-quality machine intelligence on resource-constrained devices like smartphones and wearables. It highlights innovations in architectural designs, datasets, and training algorithms that enhance SLMs' efficiency and performance, making AI more accessible.
This article explores NuExtract, a family of Small Language Models (SLMs) for extracting structured data from text. The author, Fabio Matricardi, discusses using NuExtract to process candidate CVs for a database and highlights its benefits for privacy protection and running on less powerful computers.
Explores recent trends in LLM research, including multi-modal LLMs, open-source LLMs, domain-specific LLMs, LLM agents, smaller LLMs, and Non-Transformer LLMs. Mentions examples such as OpenAI's Sora, LLM360, BioGPT, StarCoder, and Mamba.
