Jev versus text-generating models
Jev does not generate free-form text. A side-by-side video compares parallel decision-making with token-by-token text generation.
Visual explanation of how Jev works, based on Qwen2.5-RLCD
Niels Rogge created a visual explanation of how Jev works using Claude, based on the Qwen2.5-RLCD model released by Harsh Agundal on Hugging Face, replacing autoregressive LLM generation with a single Transformer decoder.
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),
· 3.8K likesOpen on X
More like this
Pricing, evaluations, and the name Jev
The launch thread explains code-and-AI workflow evaluations, the input price announced at launch, and free output tokens.
Jev Model: Decision-Making Probability Generation, 200× Faster and 400× Cheaper Than LLMs
This model is built for decision-making rather than text generation, outputting probabilities for each option in parallel instead of token-by-token responses. The author shares a simplified comparison of normal LLMs versus this Jev model.
