Long-Context Fine-Tuning with Limited VRAM
Parameter-efficient fine-tuning reduces model and optimizer memory, but dense attention still makes long training sequences expensive. We combine Hierarchical Global Attention (HGA) with segment-wise backpropagation and tiered KV storage. O
https://arxiv.org/abs/2607.15105v1 ↗Thesis fit
Good fit
Within your typical scope; diligence still required.
In your usual scope
Idea match
Light
How close the company’s idea is to your thesis statement
Sector
ai
Overlap with sectors you care about
Geography
Unknown
Location unknown — scores 0
Your thesis: “We back exceptional technical founders building AI-first products and infrastructure, deploying $100K checks within 24 hours.”
Founder → stable
Traction → stable
Idea vs market ↓ declining
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Correct facts used on the next screening or memo.
People
V
Vladimir Fedosov
2.0
low confidence
A
Aleksandr Sazhin
2.0
low confidence
A
Artemiy Grinenko
2.0
low confidence
F
Frank Woernle
2.0
low confidence
Activity & evidence
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