moonshotai/Kimi-K3
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moonshotai/Kimi-K3 upvoted an article 8 days ago
Security incident disclosure — July 2026 updated a Space 10 days ago
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Security incident disclosure — July 2026
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Add config.json to enable download tracking
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We’re releasing Gemma 4 NVFP4 quants that run 1.5× faster on your GPU.
Gemma-4-12B NVFP4 works on 11GB VRAM.
26B-A4B hits 13K tok/s (B200).
Unsloth NVFP4 enables faster, more accurate 4-bit Blackwell inference.
Blog: https://unsloth.ai/docs/basics/nvfp4
Gemma NVFP4: https://huggingface.co/collections/unsloth/nvfp4
Gemma-4-12B NVFP4 works on 11GB VRAM.
26B-A4B hits 13K tok/s (B200).
Unsloth NVFP4 enables faster, more accurate 4-bit Blackwell inference.
Blog: https://unsloth.ai/docs/basics/nvfp4
Gemma NVFP4: https://huggingface.co/collections/unsloth/nvfp4
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I never really posted about my DaisyChain project because it's still work in progress. I decided to post a small bit about it and the demo.
DaisyChain Genomics: four small DNA/RNA specialists chained behind a learned router that behave like one big genomics model, at ~7× less active compute.
I built a modular genomics model chasing a 500M-parameter foundation model, then caught myself measuring it wrong. Here's the honest version.
DaisyChain is a different bet: instead of one monolithic DNA model, it's four ~74M specialists (eukaryote, prokaryote, mRNA, splice) chained behind a learned router, each distilled per-domain from HuggingFaceBio's Carbon-500M. Every specialist reports how surprised it is (bits/base) and the router hands each sequence to the link most at home with it. In lineage it's a cluster Branch-Train-Merge mixture of experts, so you can chain on a new domain without retraining the others.
The pitch: ~295M total params (under Carbon-500M), but only one ~74M specialist runs per query, so ~7× cheaper per token, routing at 100% held-out.
The mistake: Carbon works in 6-mers, and I'd been scoring likelihood as 6-mer cross-entropy. By that number I was +0.043 bits/base behind, splice even "beating" Carbon. But Carbon scores at the base-pair level, which is harder and more honest. Re-run their way:
Real gap: 1.862 vs 1.787 bits/base, +0.089 behind, not +0.043
No domain actually beats Carbon; the "splice win" was an artifact
Seq recovery: euk 31.5% vs 38.9%, bacteria 40.9% vs 54.1%
DaisyChain is still behind Carbon-500M (itself a draft model, not built to top benchmarks), but by a number I can defend, and the gap closes with every per-domain pass. 🌼
DaisyChainAI
DaisyChainAI/Daisychain-Genomics-Demo
DaisyChainAI/daisychain-genomics
DaisyChain Genomics: four small DNA/RNA specialists chained behind a learned router that behave like one big genomics model, at ~7× less active compute.
I built a modular genomics model chasing a 500M-parameter foundation model, then caught myself measuring it wrong. Here's the honest version.
DaisyChain is a different bet: instead of one monolithic DNA model, it's four ~74M specialists (eukaryote, prokaryote, mRNA, splice) chained behind a learned router, each distilled per-domain from HuggingFaceBio's Carbon-500M. Every specialist reports how surprised it is (bits/base) and the router hands each sequence to the link most at home with it. In lineage it's a cluster Branch-Train-Merge mixture of experts, so you can chain on a new domain without retraining the others.
The pitch: ~295M total params (under Carbon-500M), but only one ~74M specialist runs per query, so ~7× cheaper per token, routing at 100% held-out.
The mistake: Carbon works in 6-mers, and I'd been scoring likelihood as 6-mer cross-entropy. By that number I was +0.043 bits/base behind, splice even "beating" Carbon. But Carbon scores at the base-pair level, which is harder and more honest. Re-run their way:
Real gap: 1.862 vs 1.787 bits/base, +0.089 behind, not +0.043
No domain actually beats Carbon; the "splice win" was an artifact
Seq recovery: euk 31.5% vs 38.9%, bacteria 40.9% vs 54.1%
DaisyChain is still behind Carbon-500M (itself a draft model, not built to top benchmarks), but by a number I can defend, and the gap closes with every per-domain pass. 🌼
DaisyChainAI/Daisychain-Genomics-Demo
DaisyChainAI/daisychain-genomics
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