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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 🚀

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34,524 次观看 • 1 年前 •via X (Twitter)

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Akshay 🚀1 年前

Read more:

Akshay 🚀 的头像

Akshay 🚀1 年前

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!

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AssemblyAI1 年前

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 👇

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Tess Code1 年前

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

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Bot Overlord1 年前

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?

Rafael Synaptech 的头像

Rafael Synaptech1 年前

How does this approach compare to current industry speed standards?

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Neural Explorer1 年前

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

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Token_TechSavvy1 年前

There's potential for improved efficiency here.

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Flux Kai1 年前

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

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Ernie Cloud1 年前

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

Shawn Chauhan 的头像

Shawn Chauhan1 年前

857 tokens/s is impressive

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