I released a softmax-free attention model at GPT-2 Medium scale (~354M params, 1

arXiv:2603.22300v1 Announce Type: cross Abstract: Scaling Transformers to ultra-long contexts is bottlenecked by the $O(n^2 d)$ cost of self-attention. Existing methods reduce

<!-- SC_OFF --><div class="md"><p>&#x200b;</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1thkwgk/bytedance_released_an_open_source_model_that/"> <img alt="bytedance released an open source model

<!-- SC_OFF --><div class="md"><p>We are open-sourcing TritonSigmoid — a fast, padding-aware sigmoid attention kernel for GPUs.</p> <p>We built this for single-cell foundation

<!-- SC_OFF --><div class="md"><p><em>Scaling language models to handle longer input sequences typically necessitates large key-value (KV) caches, resulting in substantial memory

<!-- SC_OFF --><div class="md"><p>Link to the paper:</p> <p><a

<p>Most "multimodal" AI products today are really a relay team. One model plans, then hands the job to an image model, a video model, a detector, or a robot policy. Reka's new

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s2e0d5/built_an_opensource_llm_router_for_consumer_gpus/"> <img alt="Built an open-source LLM router for