content.raw: Leave No Context Behind
content.type: text
content.value: Leave No Context Behind
content.type: new_line
content.type: new_line
content.raw: Efficient Infinite Context Transformers with Infini-attention
content.type: text
content.value: Efficient Infinite Context Transformers with Infini-attention
content.type: new_line
content.type: new_line
content.label: Leave No Context Behind: Efficient Infinite Context Transformers with
    Infini-attention (2404.07143)
content.raw: https://huggingface.co/papers/2404.07143
content.id: 2404.07143
content.type: paper
content.type: resource
content.url: https://huggingface.co/papers/2404.07143
content.type: new_line
content.type: new_line
content.raw: This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention technique dubbed Infini-attention. The Infini-attention incorporates a compressive memory into the vanilla attention mechanism and builds in both masked local attention and long-term linear attention mechanisms in a single Transformer block. We demonstrate the effectiveness of our approach on long-context language modeling benchmarks, 1M sequence length passkey context block retrieval and 500K length book summarization tasks with 1B and 8B LLMs. Our approach introduces minimal bounded memory parameters and enables fast streaming inference for LLMs.
content.type: text
content.value: This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention technique dubbed Infini-attention. The Infini-attention incorporates a compressive memory into the vanilla attention mechanism and builds in both masked local attention and long-term linear attention mechanisms in a single Transformer block. We demonstrate the effectiveness of our approach on long-context language modeling benchmarks, 1M sequence length passkey context block retrieval and 500K length book summarization tasks with 1B and 8B LLMs. Our approach introduces minimal bounded memory parameters and enables fast streaming inference for LLMs.
rawContent: Leave No Context Behind
  
  Efficient Infinite Context Transformers with Infini-attention
  
  https://huggingface.co/papers/2404.07143
  
  This work introduces an efficient method to scale Transformer-based Large Language Models (LLMs) to infinitely long inputs with bounded memory and computation. A key component in our proposed approach is a new attention technique dubbed Infini-attention. The Infini-attention incorporates a compressive memory into the vanilla attention mechanism and builds in both masked local attention and long-term linear attention mechanisms in a single Transformer block. We demonstrate the effectiveness of our approach on long-context language modeling benchmarks, 1M sequence length passkey context block retrieval and 500K length book summarization tasks with 1B and 8B LLMs. Our approach introduces minimal bounded memory parameters and enables fast streaming inference for LLMs.
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