
Explore data management strategies for effective GenAI application with QuantumBlack AI experts. Learn about challenges, solutions, and best practices for scaling impact and improving data quality in organizations.

Explore fine-tuning techniques for domain-specific LLMs, discussing strategies for feedback integration, RAG implementation, and AI infrastructure decisions to achieve significant performance improvements.

Explore prompt engineering basics with GPT-4, GPT-3.5 Turbo, and Mistral 7B through practical examples and hands-on demonstrations.

Aprenda a utilizar Langchain para resumir textos usando T5 ajustado e clustering, com demonstração prática e notebooks disponíveis.

Fine-tune a BERT foundation model using Azure Machine Learning Studio for enhanced natural language processing capabilities.

Explore GenAI's evolution, key concepts, and Vector Search implementation. Gain insights into benefits and challenges of this transformative technology in the AI landscape.

Enhance LLM capabilities using retrieval-augmented generation to access knowledge bases and deliver fact-based answers, overcoming training data limitations.

Explore multi-agent workflows using AutoGen framework for RAG-based question answering with Wikipedia integration. Learn to implement and leverage this powerful approach for dynamic information retrieval.

Witness open-source LLMs battle GPT4-Turbo in freestyle rap, showcasing linguistic creativity. Compare Wizard LM 70B, Orca-2 13B, and Mistral 7B against the advanced benchmark.

Develop a custom web search AI agent in Python. Learn why building your own agents can be more effective than using existing frameworks for AI and multi-agent workflows.

Explore sparse models' transfer performance and efficiency gains in image and language tasks, outperforming dense models even at high sparsities.

Master data extraction techniques using large language models for efficient and accurate information retrieval from various sources. Enhance your skills in JSON and YAML extraction, and compare model performances.

Explore Monte Carlo Tree Search to enhance LLM accuracy, focusing on boosting Llama 3 8B performance. Learn implementation, testing, and limitations through code examples and mathematical problem-solving.

Explore advanced RAG techniques with verified citations, including BM25 and cosine similarity methods. Learn to implement and leverage one-click document RAG for improved accuracy.

Explore challenges and design decisions for scalable language model training, with quantitative analysis of efficiency improvements using Ray, JAX pjit, and TPUv4.

Explore Alpa, a Ray-native library for automated model-parallel training of large deep learning models, optimizing execution plans and scaling complex architectures efficiently.

Explore the evolving landscape of large ML models, from ChatGPT to open-source projects. Learn how teams use W&B to accelerate development and stay ahead in the rapidly advancing field of AI.

Explore linear structures in Transformer embedding spaces, examining anisotropy, BERT's next sentence prediction, layer behavior, and multi-head attention modules.

Explore language models, covering generative AI, LLMs, encoders, prompt engineering, and transformer architecture. Gain insights into embeddings, attention mechanisms, and model limitations.

Explore language agents as a critical evolutionary step in AI, focusing on LLM research and its implications for advancing artificial intelligence capabilities.

Build a Python AI voice assistant using LiveKit and OpenAI. Learn architecture, setup, and implementation of voice recognition, text-to-speech, and AI agent functionality in this comprehensive tutorial.