DrHouseFan-315/MiniCPM5-1B-Base-GGUF
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updated a model 3 days ago
DrHouseFan-315/MiniCPM5-1B-Base-GGUF published a model 3 days ago
DrHouseFan-315/MiniCPM5-1B-Base-GGUF new activity 25 days ago
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#19 opened 25 days ago
by DrHouseFan-315
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#1 opened 3 months ago
by DrHouseFan-315
reacted to 4455henley's post with 🤯 3 months ago
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How much oil does the us use a day?
reacted to Nymbo's post with 👍 3 months ago
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We should really have a release date range slider on the /models page. Tired of "trending/most downloaded" being the best way to sort and still seeing models from 2023 on the first page just because they're embedded in enterprise pipelines and get downloaded repeatedly. "Recently Created/Recently Updated" don't solve the discovery problem considering the amount of noise to sift through.
Slight caveat: Trending actually does have some recency bias, but it's not strong/precise enough.
Slight caveat: Trending actually does have some recency bias, but it's not strong/precise enough.
- 3 replies
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Tokenizer
#4 opened 4 months ago
by DrHouseFan-315
reacted to marksverdhei's post with 😔 5 months ago
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4621
Poll: Will 2026 be the year of subquadratic attention?
The transformer architecture is cursed by its computational complexity.
It is why you run out of tokens and have to compact. But some would argue that this is a feature not a bug and that this is also why these models are so good. We've been doing a lot of research on trying to make equally good models that are computationally cheaper, But so far, none of the approaches have stood the test of time. Or so it seems.
Please vote, don't be shy. Remember that the Dunning-Kruger effect is very real, so the person who knows less about transformers than you is going to vote. We want everyone's opinion, no matter confidence.
👍 if you think at least one frontier model* will have no O(n^2) attention by the end of 2026
🔥 If you disagree
* Frontier models - models that match / outperform the flagship claude, gemini or chatgpt at the time on multiple popular benchmarks
The transformer architecture is cursed by its computational complexity.
It is why you run out of tokens and have to compact. But some would argue that this is a feature not a bug and that this is also why these models are so good. We've been doing a lot of research on trying to make equally good models that are computationally cheaper, But so far, none of the approaches have stood the test of time. Or so it seems.
Please vote, don't be shy. Remember that the Dunning-Kruger effect is very real, so the person who knows less about transformers than you is going to vote. We want everyone's opinion, no matter confidence.
👍 if you think at least one frontier model* will have no O(n^2) attention by the end of 2026
🔥 If you disagree
* Frontier models - models that match / outperform the flagship claude, gemini or chatgpt at the time on multiple popular benchmarks
- 4 replies
reacted to Alexander1337's post with 🔥🚀🧠👀 5 months ago
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