Artificial Intelligence

LoRA or full fine-tuning: which strategy to choose for your AI models?

LoRA or Full Fine-Tuning: Which Fine-Tuning Strategy Should You Choose for Your AI Models? In a world increasingly dominated by artificial intelligence (AI), adapting language models to specific business needs has become a strategic issue for many companies. Transformer models, such as LLaMA, Mistral, Phi, Qwen, or GPT, are powerful tools capable of understanding and generating text, but they often require customization to meet particular requirements. fine-tuning is one of the most effective...

Artificial Intelligence

RAG System with txtai — Part 3: Practical Case

Explore the practical applications of creating a RAG system and initiating dialogue with the Txtai model. Enhance documentation and enable seamless IT integration through indexing and querying with Txtai. Dive into part 3 of our series for insights and practical cases.

Artificial Intelligence

RAG System with txtai — Part 2: Database and Vector Indexing

In this article, we explore the use of databases for knowledge storage with TxtAi and discuss vector indexing technologies such as Elasticsearch, Faiss, Milvus, Pinecone, Weaviate, Annoy, HNSW, and Postgresql with pgvector.

Artificial Intelligence

RAG System with txtai — Part 1: Retrieval-Augmented Generation

Discover the world of "Retrieval-Augmented Generation" (RAG) - a groundbreaking fusion of information retrieval and language generation in natural language processing. Explore the key components, challenges, and opportunities of this technology, and follow a practical guide to implement a RAG system using TxtAi and Llama.cpp.

Artificial Intelligence

llama.cpp: run an LLM locally

Llama CPP is a new tool designed to execute language models, directly in C/C++. This tool is specially optimized for Apple Silicon processors through the use of ARM NEON technology and the Accelerate framework. It also offers AVX2 compatibility for systems based on x86 architectures. Operating mainly on the CPU, Llama CPP also integrates 4-bit quantization capabilities, thus enhancing its efficiency.