Beyond Market Intelligence/Qwen3

Qwen3

Qwen3 on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on qwen3 in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around qwen3, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.

We released TontaubeV1, a character-level TTS model for long-form generation [P]
Machine Learning

We released TontaubeV1, a character-level TTS model for long-form generation [P]

We're excited to announce the release of TontaubeV1, a 2.9B-parameter open-weight Text-to-Speech (TTS) model engineered for expressive speech and seamless long-form generation. Primarily supporting English and German, TontaubeV1 leverages innovative character-level tokenization and a unique chunking/position scheme to enhance performance and maintain context even in extended passages. Achieving a 50.1% score on an LLM-as-a-judge audiobook benchmark against ElevenLabs, this model represents a significant advancement in accessible AI-driven voice technology. Explore the model and demo on Hugging Face today.

How we built a SOTA search engine using PostgreSQL, pgvector, and Qwen3 embeddings [P]
Machine Learning

How we built a SOTA search engine using PostgreSQL, pgvector, and Qwen3 embeddings [P]

Papers with Code now delivers superior search results through a hybrid approach combining keyword and semantic analysis. Our system leverages PostgreSQL with pgvector for efficient vector storage, Qwen3 embeddings for nuanced text understanding, and Hugging Face's infrastructure—Jobs, Buckets, and Inference Endpoints—to power both search and related paper recommendations. This architecture, detailed in our technical breakdown, demonstrates a scalable solution for research content.