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Max Ryabinin

Together AI

20 papers hereh-index 164.6k citations22 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author8
  • last author5

Across the 18 of 20 papers where every author was matched, so the position is known.

fields
  • cs.LG9
  • cs.CL7
  • cs.CV2
  • cs.DC2
affiliations
  • Together AI
Homepage
same name
  • Max Ryabinin — 7 papers, h 5
  • Max Ryabinin — 3 papers, h 3
  • Max Ryabinin — 1 paper, h 0

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20202024
most citedFlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU

47 citations · 138 across the 18 of their papers we have counts for

collaborators
Showing 2022Show all

4 papers · 1 filter

cs.CL2022

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

BigScience Workshop, :, Teven Le Scao +391

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…

cs.CL2022★ 19 cited

RuCoLA: Russian Corpus of Linguistic Acceptability

Vladislav Mikhailov, Tatiana Shamardina, Max Ryabinin +3

Linguistic acceptability (LA) attracts the attention of the research community due to its many uses, such as testing the grammatical knowledge of language models and filtering impl…

cs.LG2022★ 6 cited

Petals: Collaborative Inference and Fine-tuning of Large Models

Alexander Borzunov, Dmitry Baranchuk, Tim Dettmers +5

Many NLP tasks benefit from using large language models (LLMs) that often have more than 100 billion parameters. With the release of BLOOM-176B and OPT-175B, everyone can download…

cs.LG2022

Training Transformers Together

Alexander Borzunov, Max Ryabinin, Tim Dettmers +5

The infrastructure necessary for training state-of-the-art models is becoming overly expensive, which makes training such models affordable only to large corporations and instituti…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.