3-letters could have made all the difference
"GPT 4.5 is not a frontier model *yet*"
@rasbt
-
GPT 4.5 frontier model status ambiguity with three letters
By
–
-
Google Paper on Machine Learning from Summer 2024
By
–
Also reminds me of this nice paper by Google last summer: https://
arxiv.org/abs/2408.03314 -
Train vs Inference Compute Scaling in LLMs: An Orthogonal Comparison
By
–
I think that's an apple-to-oranges comparison. We don't now how GPT4.5 looks like with inference-compute scaling. Train- and inference-compute are two orthogonal ways to improve LLMs.
-
GPT4.5 vs o1: Cost and Speed Comparison with Inference Scaling
By
–
what I am curious about: is GPT4.5 more expensive and slower than o1 (assuming o1 is GPT4-sized + inference-compute scaling)?
Would be interesting in the context of what GPT4.5 with o1-style inference-compute scaling will look like. -
Deploy Your Own AI Model on Cloud with Open-Source Tools
By
–
What a week! Tired of reading about other companies' AI deployments? Here's a nice hands-on tutorial to help you deploy your own AI model on a public or private cloud built on open-source tools for a change. https://t.co/8tjokHjnk9
— Sebastian Raschka (@rasbt) 28 février 2025What a week! Tired of reading about other companies' AI deployments? Here's a nice hands-on tutorial to help you deploy your own AI model on a public or private cloud built on open-source tools for a change.
-
Phi-4 Model Versions Clarification and Release Timeline
By
–
oh wow yeah, totally forgot that one. I first though those where the same Phi-4 models from December
-
Latest AI Papers: SWE-RL and LoRA Improvements
By
–
Since you all asked for this list: – 25 Feb, SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution, https://
arxiv.org/abs/2502.18449
– 24 Feb, Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment, -
Token increase, parameters, and training methods improvements
By
–
sure, but due to more tokens, more parameters, new training methods, or all of the above 😛
-
New Supervision Techniques Combined with SFT and RLHF Training
By
–
So, it's some new sort "supervision techniques" > "We trained it using new supervision techniques combined with traditional methods like supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF), similar to those used for GPT-4o."
-
Rolling dice to randomly select an LLM for daily use
By
–
Soon I'll need to roll one of these 20-sided dice to decide which LLM to use on a given day