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COMMENT PAGE FOR:
HTML ChunkLLM: A Lightweight Pluggable Framework for Accelerating LLMs Inference
Nav_Panel wrote 1 day ago:
Love it, they're teaching LLMs how to skim texts properly, which is
exactly the right approach for handling long contexts.
ProofHouse wrote 1 day ago:
wasn't this the attention sink concept to some degree? I mean it
doesn't seem out of the realm of possibility that if the latency
overhead isn't signifigant, that frontier models start adopting
similar to DeepSeek OCR tech
djoldman wrote 1 day ago:
From the results in Figure 5, it appears that this would only be
advantageous for long long contexts.
In particular, it is slower when used with <30k token context.
snowfield wrote 1 day ago:
High context is pretty normal these days though, as you keep
interfacing with the llms the context window just grows. And with
mcps and RAG is trivial to get 30k contexts++ in every query
seg_lol wrote 2 hours 49 min ago:
The system prompt for coding agents is already in the 30k range.
Vipsy wrote 1 day ago:
Seeing frameworks like this pop up reminds me how much the LLM
ecosystem is moving toward more modular and hardware-aware solutions.
Performance at lower compute cost will be key as adoption spreads past
tech giants.
Curious to see how devs plug this into real-time apps; so much room for
lightweight innovation now.
toobulkeh wrote 1 day ago:
High speed improvement (4x) with low quality loss (2%). Sounds
promising.
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