Top Tweets for #GPUPoor
every time I try to generate synthetic data with gpt/claude for alignment research, I spend 30 minutes convincing them I'm not an evil person...
bro, i cannot cause an ai apocalypse by finetuning llama-1b, trust me... #gpupoor
This baby is crawling 2 billion pages per month and hitting 1,400 tokens/s. The room stays at 30+°C (86°F) with a signature Founder Mode scent of ionized ozone and 'Eau de Silicon'.
#WebSearch #LLMs #GPUPoor #Naget
About to be slightly less #gpupoor
aku bila kena buat ai research with a single 3090
#gpupoor
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Polaris aims to eliminate the category of #GPUPOOR - Labs, teams and researchers across the globe, developed and so called “underdeveloped” countries, this is for you! - need to run a quick experiment? Need some machines? Grab a pod on Polaris. It’s super easy, beautiful, 100% free & incentivised by the most powerful intelligence network on the planet #bittensor. @TAOCommunityHub @mogmachine
In the process of generating complete frame-by-frame Catcher glove data for April 2024 (over 120,000 videos need processed). If you conduct public baseball research and would like early access (free ofc) or would like to donate compute to generate more data, reach out! #GPUPoor
LoRI is an evolution of LoRA that:
•Uses random projections & sparse masks
•Cuts (LoRA) trainable params by up to 95%
•Reduces task interference
•Enables continual & multi-task learning
•Outperforms/matches LoRA & full fine-tuning
#gpupoor
🚨 How much parameter redundancy does LoRA really contain?
We introduce LoRI, a method that keeps performance strong—even when we drastically shrink trainable parameters of LoRA. 🧵1/N
Another Tuesday morning. Another "Due to unexpected capacity constraints, Claude is unable to respond to your message. Please try again soon." #GPUpoor
.@FAL, a generative media platform for developers, has raised a $49 million Series B. https://t.co/zYwPaadWWK
🚨 New paper w/ interesting insights for #finetuning and #gpupoor folks 🚨
Full Fine-Tuning vs. LoRA for Large Language Models – Are They the Same? 🤖👇
1/ 🚀 As LLMs grow, tuning them for specific tasks gets expensive 💸 Enter Low-Rank Adaptation (LoRA)! It promises to match full fine-tuning but with fewer parameters. But are they really equivalent? MIT’s latest research sheds some light 👀
2/ Spectral Differences 🧬: LoRA introduces “intruder dimensions” in weight matrices that don’t appear in full fine-tuning. These extra dimensions change the model’s structure, meaning LoRA and full fine-tuning may leverage different parts of the parameter space 🤔
3/ Generalization & Robustness 🌐: LoRA-tuned models can forget previous knowledge and adapt less well to new tasks compared to fully fine-tuned models. Full fine-tuning holds an edge in scenarios needing robust, long-term memory.
4/ Optimal Rank for LoRA ⚙️: Turns out, LoRA’s rank matters! Mid-rank settings (e.g., r=64) perform well while retaining more of the pre-trained model’s “memory.” Low & high ranks lead to more forgetting, so adjusting parameters (like α=2r) is crucial 🔧
5/ Why It Matters 💡: LoRA offers parameter efficiency, but full fine-tuning remains best for tasks needing high adaptability. With the right tuning, LoRA can bridge some of these gaps. Each method has its place based on the task’s needs.
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