LLM Context Window
(Redirected from Model Context Limit)
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A LLM Context Window is an llm operational parameter that defines the maximum token count a large language model can process in a single llm inference request.
- AKA: Context Length, Context Size, Token Window, Attention Window, Model Context Limit.
- Context:
- It can typically constrain Input Token Processing through llm prompt length, llm document size, and llm conversation history.
- It can typically limit Information Retention via llm memory capacity, llm context preservation, and llm reference scope.
- It can typically affect Computational Requirements through llm memory usage, llm processing time, and llm attention complexity.
- It can typically determine Application Capabilitys for llm document analysis, llm multi-turn conversation, and llm code understanding.
- It can typically influence Cost Calculations via llm token pricing, llm api usage, and llm resource consumption.
- ...
- It can often enable Long-Context Applications through llm document summarization, llm book analysis, and llm codebase review.
- It can often support Retrieval-Augmented Generation with llm context injection, llm knowledge grounding, and llm fact verification.
- It can often facilitate Multi-Turn Dialogues via llm conversation memory, llm chat history, and llm session context.
- It can often accommodate Few-Shot Learning through llm example inclusion, llm demonstration prompts, and llm in-context learning.
- It can often impact Response Quality via llm context relevance, llm information access, and llm coherence maintenance.
- ...
- It can range from being a Small LLM Context Window to being a Large LLM Context Window, depending on its llm context token limit.
- It can range from being a Fixed LLM Context Window to being an Expandable LLM Context Window, depending on its llm context flexibility.
- It can range from being a Standard LLM Context Window to being an Extended LLM Context Window, depending on its llm context enhancement technique.
- It can range from being an Efficient LLM Context Window to being a Resource-Intensive LLM Context Window, depending on its llm context computational cost.
- It can range from being a Single-Document LLM Context Window to being a Multi-Document LLM Context Window, depending on its llm context document capacity.
- ...
- It can interact with Tokenization Systems through llm token encoding, llm text segmentation, and llm character mapping.
- It can utilize Attention Mechanisms via llm self-attention, llm cross-attention, and llm sparse attention.
- It can employ Context Management Strategys through llm sliding window, llm context compression, and llm selective retention.
- It can leverage Memory Optimization Techniques via llm kv-cache, llm flash attention, and llm memory pooling.
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- Example(s):
- Small Context Windows, such as:
- Medium Context Windows, such as:
- Large Context Windows, such as:
- Ultra-Large Context Windows, such as:
- 1M Token Context Window in gemini-1.5-pro models.
- 2M Token Context Window in experimental llm research models.
- 10M Token Context Window in specialized document processing models.
- ...
- Counter-Example(s):
- Model Size Parameter, which defines parameter count rather than context capacity.
- Batch Size, which specifies parallel processing rather than sequence length.
- Generation Length, which limits output tokens rather than input capacity.
- Training Sequence Length, which affects model training rather than inference context.
- See: LLM Operational Parameter, Large-Scale Language Model (LLM), Token Limit, Attention Mechanism, Retrieval-Augmented Generation, Context Management, Tokenization, LLM API Service, Transformer Architecture, Memory Optimization, Long-Context Processing.