Le architetture RNN (Recurrent Neural Network), le antenate dell'attuale Transformer, in qualche variante riescono a competere con o addirittura migliorare quest'ultima. RWKV è un esempio di particolare interesse, ed oggi è stato rilasciato un paper che descrive in dettaglio le migliorie apportate nelle ultime versioni 5 e 6.
RWKV è anche una serie di LLM
open source, non solo
open weight.
https://arxiv.org/abs/2404.05892
Quote:
[Submitted on 8 Apr 2024]
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture. Our architectural design advancements include multi-headed matrix-valued states and a dynamic recurrence mechanism that improve expressivity while maintaining the inference efficiency characteristics of RNNs. We introduce a new multilingual corpus with 1.12 trillion tokens and a fast tokenizer based on greedy matching for enhanced multilinguality. We trained four Eagle models, ranging from 0.46 to 7.5 billion parameters, and two Finch models with 1.6 and 3.1 billion parameters and find that they achieve competitive performance across a wide variety of benchmarks. We release all our models on HuggingFace under the Apache 2.0 license. Models at: https://huggingface.co/RWKV Training code at: https://github.com/RWKV/RWKV-LM Inference code at: https://github.com/RWKV/ChatRWKV Time-parallel training code at: https://github.com/RWKV/RWKV-infctx-trainer
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