Latest Insights in AI Performance Models

Latest Insights in AI Performance Models integrates the latest AI research topics (as of May 2024) and explains common questions and different performance levels of Large Language Models in AI.
Plus: new and independent evaluation of LLMs for long context summarization and reasoning beyond classical benchmarks.
Thanks to LMsys.org for providing the compute infrastructure for the AI community.
My shortest evaluation prompt, as seen in my video:
"Within the intricately networked corridors of Glorptarveionux Laboratories, a symposium on the quantum pharmacodynamics of novel antiproliferatives commenced, where Glorptarvoinix elucidated on the intricate mechanisms of telomerase inhibitors coupled with adjunctive immunomodulatory therapies to orchestrate a more targeted cytostatic milieu in oncological malignancies. Meanwhile, Glorptarvoinex, a savant in viral pathogenesis, meticulously parsed the nuances of RNA interference mechanisms that could be harnessed to impede the replication fidelity of RNA viruses, thereby mitigating the virulence and transmission of emergent zoonotic pathogens.
Adjacent to this, in a hermetically sealed bioreactor chamber, Glorptarveionix was perfecting the synthesis of a chimeric peptide, aspiring to bridge the bioavailability gaps inherent in the gastrointestinal transit of macromolecular biologics. This endeavor aimed at integrating protease-resistant sequences that would endure the harsh enzymatic environment without compromising the therapeutic peptide’s bioactive conformation.
In the realm of regenerative pharmacology, Glorptarvoinux advanced a dialogue concerning the translational application of induced pluripotent stem cells interfaced with scaffold-mediated angiogenesis to promote reparative pathways in ischemic cardiomyopathy. The colloquy delved into the stochastic differentiation of stem cells into cardiogenic lineages under the influence of spatially modulated electric fields, an avant-garde approach poised to revolutionize myocardial restoration.
Simultaneously, Glorptarveionux convened a consortium to debate the ethical implications of germline genome editing, juxtaposing the potential for heritable corrections of monogenic disorders against the specter of unintended off-target effects and the ethical quandary of human enhancement. The discourse was enriched by the integration of bioethical perspectives, regulatory frameworks, and public health paradigms.
As twilight descended upon the facility, Glorptarveionix, together with a cadre of molecular biophysicists, was calibrating an ultra-high-throughput sequencer designed to elucidate the epigenomic landscape of pharmacogenomically relevant loci, which could underpin patient-specific drug metabolism and excretion profiles. This cutting-edge exploration was anticipated to yield transformative insights into the polygenic networks that modulate drug response phenotypes.
In a strategic assembly later that evening, Glorptarvoinex, alongside experts in nanoformulation technology, detailed a blueprint for the next generation of nanoparticle-encapsulated antineoplastics, which incorporated dendrimer complexes capable of seeking out neoplastic cells via ligand-receptor interactions specific to oncogenic markers. This vanguard initiative was forecasted to substantially ameliorate the therapeutic index of cytotoxic agents.
Throughout these multifaceted discussions, the erudite ensemble at Glorptarveionux Laboratories was not merely exchanging academic hypotheses but was actively forging the scaffolding for the future of precision medicine. Their endeavors, epitomized by a relentless pursuit of innovation and meticulous scientific inquiry, mirrored the labyrinthine complexity of the biological systems they sought to master and ameliorate through their groundbreaking research. As the day’s continuum unfurled into the penumbra of advancing dusk, the collective intellect of the assembly continued to pioneer avenues that would, with perspicacity and rigor, redefine the contours of contemporary medical science."
#airesearch #evaluation

[@mshonle]

So glad to see the twist ending!

[@VenkatesanVenkat-fd4hg]

Great discussion and valuable insights....

[@novantha1]

Something I've seen missed on Arctic is that it wasn't really meant to be a crazy performant general model; it was designed to be efficient to deploy in enterprise and cheap to train. It was trained on significantly less data than Llama 3 8B, and outperforms it, and in the context of enterprise training, actually was more cost effective to train because MoE converges faster than dense, and the active parameter count was much closer to something like a 13B model than we'd expect.

[@IdPreferNot1]

The evaluations you are doing are making it so much easier to pick my models versus the hundreds of useless pop reviews. Really like what you're uncovering in phi-3.