Falcon 180B подвезли
https://falconllm.tii.ae/falcon-180b.html
Falcon 180B is a super-powerful language model with 180 billion parameters, trained on 3.5 trillion tokens. It's currently at the top of the Hugging Face Leaderboard for pre-trained Open Large Language Models and is available for both research and commercial use.
This model performs exceptionally well in various tasks like reasoning, coding, proficiency, and knowledge tests, even beating competitors like Meta's LLaMA 2.
Among closed source models, it ranks just behind OpenAI's GPT 4, and performs on par with Google's PaLM 2 Large, which powers Bard, despite being half the size of the model
https://falconllm.tii.ae/falcon-180b.html
Falcon 180B is a super-powerful language model with 180 billion parameters, trained on 3.5 trillion tokens. It's currently at the top of the Hugging Face Leaderboard for pre-trained Open Large Language Models and is available for both research and commercial use.
This model performs exceptionally well in various tasks like reasoning, coding, proficiency, and knowledge tests, even beating competitors like Meta's LLaMA 2.
Among closed source models, it ranks just behind OpenAI's GPT 4, and performs on par with Google's PaLM 2 Large, which powers Bard, despite being half the size of the model
falconllm.tii.ae
Introducing the Technology Innovation Institute’s Falcon 3 Making Advanced AI accessible and Available to Everyone, Everywhere
Falcon LLM is a generative large language model (LLM) that helps advance applications and use cases to future-proof our world.
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gonzo-обзоры ML статей
Highly accurate protein structure prediction with AlphaFold John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon…
Some updates on the AlphaFold and thoughts from the authors:
An interesting observation by Xiang Zhang:
TL;DR: number of parameters is a more determining factor than numerical precision for large language model performance. Given a memory constraint, one should maximize the number of parameters by quantizing at the highest level possible.
https://www.xzh.me/2023/09/a-perplexity-benchmark-of-llamacpp.html
TL;DR: number of parameters is a more determining factor than numerical precision for large language model performance. Given a memory constraint, one should maximize the number of parameters by quantizing at the highest level possible.
https://www.xzh.me/2023/09/a-perplexity-benchmark-of-llamacpp.html
www.xzh.me
A Perplexity Benchmark of llama.cpp
Without further ado, here are the results (explanations and discussions later): Table 1: Perplexity on wikitext-2 test set. ...
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