LLM Infini-attention - Revision history

XenoEngineer at 11:41, 18 June 2025

2025-06-18T11:41:22Z

← Older revision		Revision as of 11:41, 18 June 2025
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	By combining these mechanisms, the LLM can efficiently handle inputs of infinite length by utilizing a combination of local context and long-term memory representations. This enables the model to capture both short-range and long-range dependencies in the input sequence, improving its ability to understand and generate responses appropriate for much longer texts or tasks.		By combining these mechanisms, the LLM can efficiently handle inputs of infinite length by utilizing a combination of local context and long-term memory representations. This enables the model to capture both short-range and long-range dependencies in the input sequence, improving its ability to understand and generate responses appropriate for much longer texts or tasks.

	The concept of infinite attention is particularly useful for applications that require handling lengthy texts, such as language generation, translation, or even ~~sumarization~~ tasks where understanding the overall context is crucial. By combining local and global attention in this way, LLMs can better model complex relationships within long sequences and provide more coherent and accurate responses accordingly.		The concept of infinite attention is particularly useful for applications that require handling lengthy texts, such as language generation, translation, or even summarization tasks where understanding the overall context is crucial. By combining local and global attention in this way, LLMs can better model complex relationships within long sequences and provide more coherent and accurate responses accordingly.
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XenoEngineer: XenoEngineer moved page LLM Infinite Attention to LLM Infini-attention

2024-09-16T12:41:30Z

XenoEngineer moved page LLM Infinite Attention to LLM Infini-attention

← Older revision	Revision as of 12:41, 16 September 2024
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XenoEngineer at 12:55, 13 August 2024

2024-08-13T12:55:40Z

← Older revision		Revision as of 12:55, 13 August 2024
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	[[Category:infinite Attention]]		[[Category:infinite Attention]]
	{{menuAIScribe}}		{{menuAIScribe}}
	<div style="background-color:azure; border:1px outset azure; padding:0 20px; max-width:~~860px~~; margin:0 auto; ">		<div style="background-color:azure; border:1px outset azure; padding:0 20px; max-width:836px; margin:0 auto; ">
	Infinite attention is a mechanism that enables large language models (LLMs) to efficiently process inputs with an infinite length, or at least inputs much longer than the standard maximum input sequence length used in typical transformer-based LLMs.		Infinite attention is a mechanism that enables large language models (LLMs) to efficiently process inputs with an infinite length, or at least inputs much longer than the standard maximum input sequence length used in typical transformer-based LLMs.

XenoEngineer at 22:02, 24 July 2024

2024-07-24T22:02:37Z

← Older revision		Revision as of 22:02, 24 July 2024
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	;Here's how it works at a high level:		;Here's how it works at a high level:

	1. Local Attention: The LLM first applies standard local attention mechanisms to focus on relevant tokens in close proximity, similar to how traditional transformers process input sequences.		'''1. Local Attention:''' The LLM first applies standard local attention mechanisms to focus on relevant tokens in close proximity, similar to how traditional transformers process input sequences.

	2. Long-Term Memory: Additionally, the model maintains a separate long-term memory module that stores and updates representations of all input tokens as they are encountered. This memory is updated dynamically as new tokens are added, providing a summary or compressed representation of the past inputs.		'''2. Long-Term Memory:''' Additionally, the model maintains a separate long-term memory module that stores and updates representations of all input tokens as they are encountered. This memory is updated dynamically as new tokens are added, providing a summary or compressed representation of the past inputs.

	3. Global Attention: With both local attention and long-term memory in place, the model can then apply global attention mechanisms. This allows the LLM to attend to all input tokens simultaneously, including those further away from the current focus or context.		'''3. Global Attention:''' With both local attention and long-term memory in place, the model can then apply global attention mechanisms. This allows the LLM to attend to all input tokens simultaneously, including those further away from the current focus or context.

	By combining these mechanisms, the LLM can efficiently handle inputs of infinite length by utilizing a combination of local context and long-term memory representations. This enables the model to capture both short-range and long-range dependencies in the input sequence, improving its ability to understand and generate responses appropriate for much longer texts or tasks.		By combining these mechanisms, the LLM can efficiently handle inputs of infinite length by utilizing a combination of local context and long-term memory representations. This enables the model to capture both short-range and long-range dependencies in the input sequence, improving its ability to understand and generate responses appropriate for much longer texts or tasks.

XenoEngineer at 15:59, 22 July 2024

2024-07-22T15:59:29Z

← Older revision		Revision as of 15:59, 22 July 2024
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	[[Category:AI]]		[[Category:AI]]
	[[Category:infinite Attention]]		[[Category:infinite Attention]]
			{{menuAIScribe}}
	<div style="background-color:azure; border:1px outset azure; padding:0 20px; max-width:860px; margin:0 auto; ">		<div style="background-color:azure; border:1px outset azure; padding:0 20px; max-width:860px; margin:0 auto; ">
	Infinite attention is a mechanism that enables large language models (LLMs) to efficiently process inputs with an infinite length, or at least inputs much longer than the standard maximum input sequence length used in typical transformer-based LLMs.		Infinite attention is a mechanism that enables large language models (LLMs) to efficiently process inputs with an infinite length, or at least inputs much longer than the standard maximum input sequence length used in typical transformer-based LLMs.

XenoEngineer at 15:22, 22 July 2024

2024-07-22T15:22:59Z

← Older revision		Revision as of 15:22, 22 July 2024
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	<div style="background-color:azure; border:1px outset azure; padding:0 20px; max-width:860px; margin:0 auto; ">		<div style="background-color:azure; border:1px outset azure; padding:0 20px; max-width:860px; margin:0 auto; ">
	Infinite attention is a mechanism that enables large language models (LLMs) to efficiently process inputs with an infinite length, or at least inputs much longer than the standard maximum input sequence length used in typical transformer-based LLMs.		Infinite attention is a mechanism that enables large language models (LLMs) to efficiently process inputs with an infinite length, or at least inputs much longer than the standard maximum input sequence length used in typical transformer-based LLMs.

	The core idea behind infinite attention is to provide a way for the model to attend to and utilize all available input tokens equally, regardless of their position in the sequence. This is achieved by introducing a "compressive long-term memory" module that keeps track of past inputs and provides a summary representation for later use.		<big>'''The core idea behind infinite attention is to provide a way for the model to attend to and utilize all available input tokens equally, regardless of their position in the sequence. This is achieved by introducing a "compressive long-term memory" module that keeps track of past inputs and provides a summary representation for later use.'''</big>

	Here's how it works at a high level:		;Here's how it works at a high level:

	1. Local Attention: The LLM first applies standard local attention mechanisms to focus on relevant tokens in close proximity, similar to how traditional transformers process input sequences.		1. Local Attention: The LLM first applies standard local attention mechanisms to focus on relevant tokens in close proximity, similar to how traditional transformers process input sequences.

			2. Long-Term Memory: Additionally, the model maintains a separate long-term memory module that stores and updates representations of all input tokens as they are encountered. This memory is updated dynamically as new tokens are added, providing a summary or compressed representation of the past inputs.

			3. Global Attention: With both local attention and long-term memory in place, the model can then apply global attention mechanisms. This allows the LLM to attend to all input tokens simultaneously, including those further away from the current focus or context.

	2. Long-Term Memory: Additionally, the model maintains a separate long-term memory module that stores and updates representations of all input tokens as they are encountered. This memory is updated dynamically as new tokens are added, providing a summary or compressed representation of the past inputs.		By combining these mechanisms, the LLM can efficiently handle inputs of infinite length by utilizing a combination of local context and long-term memory representations. This enables the model to capture both short-range and long-range dependencies in the input sequence, improving its ability to understand and generate responses appropriate for much longer texts or tasks.

	3. Global Attention: With both local attention and long-term memory in place, the model can then apply global attention mechanisms. This allows the LLM to attend to all input tokens simultaneously, including those further away from the current focus or context.

	By combining these mechanisms, the LLM can efficiently handle inputs of infinite length by utilizing a combination of local context and long-term memory representations. This enables the model to capture both short-range and long-range dependencies in the input sequence, improving its ability to understand and generate responses appropriate for much longer texts or tasks.

	The concept of infinite attention is particularly useful for applications that require handling lengthy texts, such as language generation, translation, or even sumarization tasks where understanding the overall context is crucial. By combining local and global attention in this way, LLMs can better model complex relationships within long sequences and provide more coherent and accurate responses accordingly.		The concept of infinite attention is particularly useful for applications that require handling lengthy texts, such as language generation, translation, or even sumarization tasks where understanding the overall context is crucial. By combining local and global attention in this way, LLMs can better model complex relationships within long sequences and provide more coherent and accurate responses accordingly.
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XenoEngineer: Created page with "Category:LLM Category:AI Category:infinite Attention
Infinite attention is a mechanism that enables large language models (LLMs) to efficiently process inputs with an infinite length, or at least inputs much longer than the standard maximum input sequence length used in typical transformer-based LLMs. The core idea behind infinite attention is to..."

2024-07-22T15:19:45Z

Created page with "Category:LLM Category:AI Category:infinite Attention <div style="background-color:azure; border:1px outset azure; padding:0 20px; max-width:860px; margin:0 auto; "> Infinite attention is a mechanism that enables large language models (LLMs) to efficiently process inputs with an infinite length, or at least inputs much longer than the standard maximum input sequence length used in typical transformer-based LLMs. The core idea behind infinite attention is to..."

New page

[[Category:LLM]]
[[Category:AI]]
[[Category:infinite Attention]]

<div style="background-color:azure; border:1px outset azure; padding:0 20px; max-width:860px; margin:0 auto; ">
Infinite attention is a mechanism that enables large language models (LLMs) to efficiently process inputs with an infinite length, or at least inputs much longer than the standard maximum input sequence length used in typical transformer-based LLMs.

The core idea behind infinite attention is to provide a way for the model to attend to and utilize all available input tokens equally, regardless of their position in the sequence. This is achieved by introducing a "compressive long-term memory" module that keeps track of past inputs and provides a summary representation for later use.

Here's how it works at a high level:

1. **Local Attention**: The LLM first applies standard local attention mechanisms to focus on relevant tokens in close proximity, similar to how traditional transformers process input sequences.

2. **Long-Term Memory**: Additionally, the model maintains a separate long-term memory module that stores and updates representations of all input tokens as they are encountered. This memory is updated dynamically as new tokens are added, providing a summary or compressed representation of the past inputs.

3. **Global Attention**: With both local attention and long-term memory in place, the model can then apply global attention mechanisms. This allows the LLM to attend to all input tokens simultaneously, including those further away from the current focus or context.

By combining these mechanisms, the LLM can efficiently handle inputs of infinite length by utilizing a combination of local context and long-term memory representations. This enables the model to capture both short-range and long-range dependencies in the input sequence, improving its ability to understand and generate responses appropriate for much longer texts or tasks.

The concept of infinite attention is particularly useful for applications that require handling lengthy texts, such as language generation, translation, or even sumarization tasks where understanding the overall context is crucial. By combining local and global attention in this way, LLMs can better model complex relationships within long sequences and provide more coherent and accurate responses accordingly.
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