LoRA
LoRA on Beyond Market Intelligence: a running collection of 3 stories we have gathered and hand-picked because they are worth your time. Every post here touches on lora in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around lora, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Same effective batch does not mean same training time with gradient accumulation, tested on LoRA on T4 and L4 [D]
Contrary to initial assumptions, achieving the same effective batch size through gradient accumulation doesn't guarantee equivalent training times. Recent experimentation with Qwen3-1.7B and LoRA on T4 and L4 GPUs revealed significant performance variations – up to a 41% difference – based on batch shape (1x4 vs. 4x1). While effective batch influences optimization behavior, physical batch size impacts GPU execution patterns, affecting forward and backward pass efficiency. As highlighted in Hugging Face documentation, optimizing for memory and speed requires treating these as distinct choices.
New ransomware targets AI model weights and can't even collect the ransom
A new ransomware strain, ENCFORGE, is specifically targeting AI model weights, marking a concerning evolution in cyberattacks. Unlike generic ransomware, ENCFORGE actively seeks out and encrypts crucial AI assets like PyTorch checkpoints and Hugging Face weights, recognizing their irreplaceable value. Exploiting a known vulnerability (CVE-2025-3248) in Langflow, the attacker demonstrated the ability to rapidly compromise systems and exfiltrate credentials, ultimately prioritizing data destruction over ransom demands.
Reproducing OpenAI’s “persistently beneficial models” - GRPO trait install barely moves. Ideas? [P] [R]
Reproducing OpenAI’s trait-persistence results presents a significant challenge, particularly at smaller scales. Our attempt to install a "traditionalism" trait (low Openness) using GRPO on Qwen2-7B achieved a minimal improvement of just +2.4 points, falling far short of the ~+15 needed. Despite rigorous debugging—ruling out reward hacking, memorization, and gradient issues—the install remains stubbornly flat. We’re seeking guidance from those with experience in small-scale RLHF/GRPO for trait or persona installation. See "The qlora 2e-4 default is wrong under 10k samples and nobody