| ๐ rwkv | ๐ chatglm | |
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ๆจกๅๆถๆ | ๐ก Receptance Weighted Key Value (RWKV) RWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). It combines the efficient parallelizable training of GPT transformer with the efficient inference aspect of RNN. RWKV is 100% attention-free, inspired by Apple's Attention Free Transformer (AFT). The architecture has been carefully simplified and optimized to achieve high performance. RWKV uses a linear attention mechanism, allowing for efficient parallelization and inference. | ๐ง ChatGLM-6B ChatGLM-6B is an open bilingual language model based on the General Language Model (GLM) framework, with 6.2 billion parameters. It is trained on 1T tokens of English and Chinese. ChatGLM-6B aims to advance and democratize artificial intelligence through open source and open science. It supports both English and Chinese language for chat-based question answering.
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ๆง่ฝ่กจ็ฐ | ๐ Transformer-level LLM performance RWKV achieves Transformer-level Language Model (LLM) performance, which is known for its state-of-the-art performance in various natural language processing tasks. It can generate high-quality and coherent text, making it suitable for applications such as text generation, chatbots, and language understanding. RWKV's performance is comparable to models like GPT transformer, but with the added advantage of being directly trainable like GPT transformer. | ๐ Limited Performance ChatGLM-6B, although based on the General Language Model (GLM) framework, has limited performance compared to RWKV. It may not achieve the same level of language understanding and text generation capabilities as RWKV. The model's performance may be affected by the size of the training data and the complexity of the language tasks it is applied to.
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่ฎญ็ปๆ็ | โก๏ธ Parallelizable Training RWKV can be directly trained like a GPT transformer, which allows for efficient parallelizable training. This means that the training process can be accelerated by utilizing multiple GPUs or distributed computing resources. The parallel training capability of RWKV enables faster model development and experimentation, making it suitable for research and industry applications. | ๐ข Non-parallelizable Training ChatGLM-6B may have limitations in terms of training efficiency. As it is not explicitly mentioned, it is likely that the training process of ChatGLM-6B is not parallelizable. This can result in slower training times and longer development cycles, especially when dealing with large-scale language models. Non-parallelizable training can be a bottleneck for research and industry applications that require fast model iteration and deployment.
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ๆณจๆๅๆบๅถ | ๐ 100% Attention-Free RWKV stands out by being 100% attention-free. It does not rely on attention mechanisms, which are computationally expensive and can limit the scalability of models. RWKV's linear attention mechanism allows for efficient parallelization and inference, making it suitable for large-scale language models. The attention-free design of RWKV reduces computational complexity and enables faster inference times, making it a favorable choice for real-time applications. | ๐ค Attention Mechanism ChatGLM-6B, being based on the General Language Model (GLM) framework, likely relies on attention mechanisms for language understanding and generation. Attention mechanisms can introduce computational overhead and limit the scalability of models, especially when dealing with large-scale language models. The attention mechanism in ChatGLM-6B may result in slower inference times and higher computational requirements compared to RWKV's attention-free design. |