A technique for deterministic labeling from stochastic models, with benchmarked Golang implementation.

A guide on how to convert .epub e-books into high-quality audiobooks narrated by neural text-to-speech

Use cases, techniques, alignment, finetuning, and critiques against LLM-evaluators.

txtai is an all-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows

Deep Infra offers cost-effective, scalable, easy-to-deploy, and production-ready machine-learning models and infrastructures for deep-learning models.

This is Dot, a standalone open source app meant for easy use of local LLMs and RAG in particular to interact with documents and files similarly to Nvidia’s Chat with RTX. Dot itself is completely standalone and is packaged with all dependencies including a copy of Mistral 7B, this is to ensure the app is as accessible as possible and no prior knowledge of programming or local LLMs is required to use it.

Powerful yet Compact: Browser-based AI models for a wide array of tasks, designed for optimal efficiency and user-friendliness, ensuring your privacy.

Dataherald is a natural language-to-SQL engine built for enterprise-level question answering over structured data. It allows you to set up an API from your database that can answer questions in plain English.

Get up and running with large language models, locally.

In this post, we’ll talk about why fine-tuning is probably not necessary for your app, and why applying two of the most common techniques to the base GPT models — few-shot prompting and retrieval-augmented generation (RAG) — are sufficient for most use cases.