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Armand Szabó

ChiefTheLord
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AI & ML interests

Frozen Vision Language Alignment 🧊

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reacted to mike-ravkine's post with 🔥 about 1 hour ago
Happy 2026 everyone! I've been busy working on some new ranking/position methodologies and excited to start sharing some results. Plot legends: - X = truncation rate (low = good) - ? = confusion rate (low = good) - blue bars = average completion tokens (low = good) - black diamonds = CI-banded performance (high = good) - cluster squares = models inside this group are equivalent https://huggingface.co/openai/gpt-oss-120b remains the king in all dimensions of interest: truncation rates, completion lengths and performance. If I had but one complaint it's the reason_effort does not seem to actually work - more on this soon. Second is a 3-way tie in performance between the Qwen3-235B-2507 we all know and love with an unexpected entrant - https://huggingface.co/ByteDance-Seed/Seed-OSS-36B-Instruct This is a very capable model and it's reasoning effort controls actually works, but you should absolutely not leave it on the default "unlimited" - enable a sensible limit (4k works well for 8k context length). Third place is another 3-way tie, this one between Seed-OSS-36B (it straddles the CI boundary between 2nd and 3rd place), https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Instruct (demonstrating that full attention may be overrated after all and gated is the way to go) and the newly released https://huggingface.co/zai-org/GLM-4.7 which offers excellent across the board performance with some of the shortest reasoning traces I've seen so far.
updated a collection about 6 hours ago
AutoEncoderPostEval
updated a collection about 6 hours ago
AutoEncoderPostEval
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