AlexanderKyng/Qwopus-27B-Coder-Mixed-Q5
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Alexander
AlexanderKyng
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AI & ML interests
Deeply passionate in all things AI. Junior Data Scientist & AI Engineer.
Recent Activity
updated a model about 3 hours ago
AlexanderKyng/Qwopus-27B-Coder-Mixed-Q5 reacted to Banaxi-Tech's post with ๐ about 6 hours ago
A new model is coming!
Its going to take a long time on my 5070 Ti so expect a release in ~1 month.
We think this model is going to be SOTA For its size.
Our Mini Version will be 25M Parameters and Pro with 140M.
The Pro version has a 3072 Context Window (Extensible to up to 6K with RoPE) And the Mini version has a context window of 4096 (Up to 8K with RoPE)
Meanwhile we are currently working on a Instruct Version of our BananaMind 1.5 Base.
The training will start this weekend
We are very exited to release it when its done! published a model 1 day ago
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updated a
model about 3 hours ago
reacted to Banaxi-Tech's post with ๐ about 6 hours ago
Post
4090
A new model is coming!
Its going to take a long time on my 5070 Ti so expect a release in ~1 month.
We think this model is going to be SOTA For its size.
Our Mini Version will be 25M Parameters and Pro with 140M.
The Pro version has a 3072 Context Window (Extensible to up to 6K with RoPE) And the Mini version has a context window of 4096 (Up to 8K with RoPE)
Meanwhile we are currently working on a Instruct Version of our BananaMind 1.5 Base.
The training will start this weekend
We are very exited to release it when its done!
Its going to take a long time on my 5070 Ti so expect a release in ~1 month.
We think this model is going to be SOTA For its size.
Our Mini Version will be 25M Parameters and Pro with 140M.
The Pro version has a 3072 Context Window (Extensible to up to 6K with RoPE) And the Mini version has a context window of 4096 (Up to 8K with RoPE)
Meanwhile we are currently working on a Instruct Version of our BananaMind 1.5 Base.
The training will start this weekend
We are very exited to release it when its done!
- 2 replies
published a
model 1 day ago
reacted to ginigen-ai's post with ๐ฅโค๏ธ 1 day ago
Post
7478
๐ง Does your LLM know when it's about to be wrong?
Most leaderboards measure accuracy. We measure metacognition โ whether a model catches its own errors. Benchmark + leaderboard + adapters, all open. ๐
The surprise: even a K-AI #1 model (JGOS-31B-Citizen) is the strongest on multiple-choice traps (trap_rate 0.005 โ ~2 misses in 400) yet blind to its own free-form mistakes (self-confidence AUROC = 0.5, pure random). A tiny base-frozen adapter recovers that signal.
Two independent axes (never compared across a row): โ trap_rate โ does it fall for tempting trap options? (lower = stronger) โก adapter gain ฮ โ how much a lightweight adapter catches errors the model itself misses. (higher = more adapter value)
What's open: ๐ 300+100 trap problems (each with a hidden trap + TICOS type) ๐ 24-model leaderboard ๐งฉ 11 per-model adapters โ adapters, NOT fine-tunes (base stays frozen; the adapter just reads the hidden state โ P(wrong))
Submit any HF model โ auto-scored daily at 09:00 KST and added to the board.
๐ Leaderboard โ ginigen-ai/Metacognition-Leaderboard-Space
๐ Benchmark โ ginigen-ai/Metacognition-Bench
๐งฉ Adapters โ FINAL-Bench/metacognition-adapters-6a42c032e6beb803dd032961
๐ Article โ https://huggingface.co/blog/ginigen-ai/metacognition
Benchmark by ginigen-ai ยท Adapters by FINAL-Bench (Darwin/Chimera platform + AETHER metacognition tech).
Most leaderboards measure accuracy. We measure metacognition โ whether a model catches its own errors. Benchmark + leaderboard + adapters, all open. ๐
The surprise: even a K-AI #1 model (JGOS-31B-Citizen) is the strongest on multiple-choice traps (trap_rate 0.005 โ ~2 misses in 400) yet blind to its own free-form mistakes (self-confidence AUROC = 0.5, pure random). A tiny base-frozen adapter recovers that signal.
Two independent axes (never compared across a row): โ trap_rate โ does it fall for tempting trap options? (lower = stronger) โก adapter gain ฮ โ how much a lightweight adapter catches errors the model itself misses. (higher = more adapter value)
What's open: ๐ 300+100 trap problems (each with a hidden trap + TICOS type) ๐ 24-model leaderboard ๐งฉ 11 per-model adapters โ adapters, NOT fine-tunes (base stays frozen; the adapter just reads the hidden state โ P(wrong))
Submit any HF model โ auto-scored daily at 09:00 KST and added to the board.
๐ Leaderboard โ ginigen-ai/Metacognition-Leaderboard-Space
๐ Benchmark โ ginigen-ai/Metacognition-Bench
๐งฉ Adapters โ FINAL-Bench/metacognition-adapters-6a42c032e6beb803dd032961
๐ Article โ https://huggingface.co/blog/ginigen-ai/metacognition
Benchmark by ginigen-ai ยท Adapters by FINAL-Bench (Darwin/Chimera platform + AETHER metacognition tech).
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