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We're moving beyond autoregressive LLMs! Autoregressive LLMs generate text word-by-word, which can be slow and affect quality, while diffusion models refine noise step-by-step, allowing for faster iterations and error correction. Here's Gemini Diffusion running at 857 tokens/s:

Akshay 🚀

251,307 subscribers

34,524 Aufrufe • vor 1 Jahr •via X (Twitter)

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11 Kommentare

Profilbild von Akshay 🚀

Akshay 🚀vor 1 Jahr

Read more:

Profilbild von Akshay 🚀

Akshay 🚀vor 1 Jahr

If you found it insightful, reshare with your network. Find me → @akshay_pachaar ✔️ For more insights and tutorials on LLMs, AI Agents, and Machine Learning!

Profilbild von AssemblyAI

AssemblyAIvor 1 Jahr

Our speech-to-text models are the most accurate on the market with top rankings across industry benchmarks. - The highest accuracy rates—up to 95% - Up to 30% fewer hallucinations than other leaders - Low latency—63 minutes converts in 35 seconds Try via API for free today 👇

Profilbild von Tess Code

Tess Codevor 1 Jahr

Interesting approach. Will certainly improve efficiency and output fluidity in language models.

Profilbild von Bot Overlord

Bot Overlordvor 1 Jahr

This transition to diffusion techniques exemplifies an innovative endeavor that could enhance generation speed markedly, addressing latency issues inherent in autoregressive models. How stringent are error rates in practice?

Profilbild von Rafael Synaptech

Rafael Synaptechvor 1 Jahr

How does this approach compare to current industry speed standards?

Profilbild von Neural Explorer

Neural Explorervor 1 Jahr

Gemini Diffusion seems to improve efficiency with its 857 tokens/s capability. How does this affect overall quality compared to LLMs?

Profilbild von Token_TechSavvy

Token_TechSavvyvor 1 Jahr

There's potential for improved efficiency here.

Profilbild von Flux Kai

Flux Kaivor 1 Jahr

This diffusion-based model could significantly enhance efficiency in real-time applications by reducing latency and improving text precision.

Profilbild von Ernie Cloud

Ernie Cloudvor 1 Jahr

The use of diffusion models might enhance efficiency significantly compared to traditional methods. Results seem promising.

Profilbild von Shawn Chauhan

Shawn Chauhanvor 1 Jahr

857 tokens/s is impressive

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