GitHub - BlinkDL/RWKV-LM: RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
GitHub - BlinkDL/RWKV-LM: RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.https://github.com/BlinkDL/RWKV-LM

GitHub - BlinkDL/RWKV-LM: RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.

🚀 Discover RWKV-LM: An AI tool that blends RNN & Transformer, offering top-notch performance & fast training. With infinite context length & free sentence embeddings, it's a game-changer in NLP! 🤖🔥 #AI #NLP #RWKVLM #TransformingNLP

  • RWKV is an RNN with Transformer-level LLM performance, designed to be directly trained like a GPT transformer, parallelizable, and 100% attention-free.
  • RWKV incorporates the best features of RNNs and Transformers, offering great performance, fast inference, VRAM efficiency, faster training, "infinite" context length, and free sentence embeddings.
  • The RWKV Language Model (RWKV-LM) uses a unique time-mix and channel-mix layers approach, decomposing attention into R * W * K components to achieve effective context processing.
  • RWKV's token-shift mechanism adds a residual connection-like effect, enhancing context propagation within the model.
  • RWKV introduces the Head-QK trick to help improve token copying and avoiding in the context, allowing the model to learn NER-like tasks.
  • RWKV utilizes a new sampling method called top-a to dynamically adjust sampling probabilities based on the maximum probability in the distribution.
  • The model is VRAM-friendly, efficient for character-level tasks, and can be initialized carefully for faster convergence using orthogonal matrices and specialized scaling techniques.
  • Contributors to the RWKV project focus on enhancing model performance, training efficiency, and innovative model design to push the boundaries of natural language processing.
  • RWKV models demonstrate strong performance in various tasks, including character-level tasks, and stand out for their unique design principles and effectiveness in real-world applications.