For anyone curious how Jev works, I made a visual explanation using @claudeai :)
This is a AI post classified by Jev as AI research (a tutorial), kept by the AI Radar because it carries real work, not commentary.
For anyone curious how Jev works, I made a visual explanation using @claudeai :) This is based on the Qwen2.5-RLCD model which @harshagundal released on @huggingface The idea is to replace autoregressive LLM generation by a single Transformer decoder (of a pre-trained LLM), which processes the context + JSON schema only once. The keys and values of those tokens are cached. Next, for each field of the JSON schema, we: 1. pass its field suffix tokens through the Transformer decoder again (reusing the KV-cache) 2. obtain a final hidden state, which we pass through the language modeling head 3
Posted by Niels Rogge (24k followers) 3 days ago · 3.7k likes · 386.2k views · view the original post on X. Kept by the AI Radar as AI research.
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