AI Tutorials

Explore OpenAI's text-embedding-3-large and -small models in our guide to enhancing NLP tasks with cutting-edge AI embeddings for developers and researchers.

A comprehensive guide to Hugging Face Text Generation Inference for self-hosting large language models on local devices.

This step-by-step tutorial offers an in-depth exploration of how to harness the full capabilities of GPT-4, enhancing its performance for specialized tasks through fine-tuning.

Learn about the Claude 3 models, detailed performance benchmarks, and how to access them. Additionally, discover the new Claude 3 Python API for generating text, accessing vision capabilities, and streaming.

Discover the power of Mamba LLM, a transformative architecture from leading universities, redefining sequence processing in AI.

A step-by-step guide to building semantic search applications using OpenAI and Pinecone in Python.

Explore using Canopy as an open-source Retrieval Augmented Generation (RAG) framework and context built on top of the Pinecone vector database.

Discover how LangSmith optimizes LLM testing and debugging for AI applications. Enhance quality assurance and streamline development with real-world examples.

Most open-source LLMs have one huge drawback - short context length. Since context length is essentially the “memory” of LLMs, this issue needs to be addressed urgently. That’s exactly what Salesforce XGen 7B LLM does - provide an impressive 8k context length. This article is a tutorial on how to use and fine-tune it.

Learn how fine-tuning large language models (LLMs) improves their performance in tasks like language translation, sentiment analysis, and text generation.

Discover everything you need to know about LMQL, short for Language Models Query Language, an innovative programming language for LLMs.

Learn an end-to-end workflow to monitor any model in your Jupyter notebook in production environments.