Python tutorial for reproducible labeling of cutting-edge topic models with GPT4-o-mini. The article details training a FASTopic model and labeling its results using GPT-4.0 mini, emphasizing reproducibility and control over the labeling process.
A post with pithy observations and clear conclusions from building complex LLM workflows, covering topics like prompt chaining, data structuring, model limitations, and fine-tuning strategies.
This article details the often overlooked cost of storing embeddings for RAG systems, and how quantization techniques (int8 and binary) can significantly reduce storage requirements and improve retrieval speed without substantial accuracy loss.
This Space demonstrates a simple method for embedding text using a LLM (Large Language Model) via the Hugging Face Inference API. It showcases how to convert text into numerical vector representations, useful for semantic search and similarity comparisons.
This study demonstrates that neural activity in the human brain aligns linearly with the internal contextual embeddings of speech and language within large language models (LLMs) as they process everyday conversations.
This tutorial demonstrates how to build a powerful document search engine using Hugging Face embeddings, Chroma DB, and Langchain for semantic search capabilities.
The attention mechanism in Large Language Models (LLMs) helps derive the meaning of a word from its context. This involves encoding words as multi-dimensional vectors, calculating query and key vectors, and using attention weights to adjust the embedding based on contextual relevance.
Qodo-Embed-1-1.5B is a state-of-the-art code embedding model designed for retrieval tasks in the software development domain. It supports multiple programming languages and is optimized for natural language-to-code and code-to-code retrieval, making it highly effective for applications such as code search and retrieval-augmented generation.
Qodo releases Qodo-Embed-1-1.5B, an open-source code embedding model that outperforms competitors from OpenAI and Salesforce, enhancing code search, retrieval, and understanding for enterprise development teams.
This article provides a comprehensive guide on the basics of BERT (Bidirectional Encoder Representations from Transformers) models. It covers the architecture, use cases, and practical implementations, helping readers understand how to leverage BERT for natural language processing tasks.
