allenai/OLMo-2-1124-7B
7B • Updated • 43.9k • 66
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null | null | {"provenance":"001.jsonl.gz:1"} | null | \section{Introduction}
With the explosive growth of Internet of Things (IoT) devices, wireless communication networks (WCNs) are increasingly facing the challenge of allocating finite transmit power and bandwidth for system utility maximization~\cite{xu2021survey}. Accordingly, one needs to design advanced radio resou... | null | null | null | proofpile-arXiv_065-0 | {"arxiv_id":"2112.01738","language":"en","timestamp":1656987826000,"url":"https:\/\/arxiv.org\/abs\/2112.01738","yymm":"2112"} | 2024-02-18T23:39:39.769Z | 2022-07-05T02:23:46.000Z | null | {"paloma_paragraphs":[]} | null |
null | null | {"provenance":"001.jsonl.gz:2"} | null | \section{Introduction}
Vector Quantised Variational AutoEncoder (VQ-VAE) ~\cite{van2017neural} is a popular method developed to compress images into discrete representations for the generation. Typically, after the compression and discretization representation by the convolutional network, an autoregressive model i... | null | null | null | proofpile-arXiv_065-1 | {"arxiv_id":"2112.01799","language":"en","timestamp":1638757008000,"url":"https:\/\/arxiv.org\/abs\/2112.01799","yymm":"2112"} | 2024-02-18T23:39:39.773Z | 2021-12-06T02:16:48.000Z | null | {"paloma_paragraphs":[]} | null |
null | null | {"provenance":"001.jsonl.gz:3"} | null | "\\section{Introduction}\nBlazars are the most extreme subclass of active galactic nuclei (AGN) with(...TRUNCATED) | null | null | null | proofpile-arXiv_065-2 | "{\"arxiv_id\":\"2112.01739\",\"language\":\"en\",\"timestamp\":1638756796000,\"url\":\"https:\\/\\/(...TRUNCATED) | 2024-02-18T23:39:39.775Z | 2021-12-06T02:13:16.000Z | null | {"paloma_paragraphs":[]} | null |
null | null | {"provenance":"001.jsonl.gz:4"} | null | "\\section{Introduction}\n\\label{intro}\nThe astrophysical plasmas characterized by high Lundquist (...TRUNCATED) | null | null | null | proofpile-arXiv_065-3 | "{\"arxiv_id\":\"2112.01785\",\"language\":\"en\",\"timestamp\":1638756959000,\"url\":\"https:\\/\\/(...TRUNCATED) | 2024-02-18T23:39:39.779Z | 2021-12-06T02:15:59.000Z | null | {"paloma_paragraphs":[]} | null |
null | null | {"provenance":"001.jsonl.gz:5"} | null | "\\section{Introduction}\\label{sec:intro}\n\n\nSpace provides a useful vantage point for monitoring(...TRUNCATED) | null | null | null | proofpile-arXiv_065-4 | "{\"arxiv_id\":\"2112.01723\",\"language\":\"en\",\"timestamp\":1638756703000,\"url\":\"https:\\/\\/(...TRUNCATED) | 2024-02-18T23:39:39.782Z | 2021-12-06T02:11:43.000Z | null | {"paloma_paragraphs":[]} | null |
null | null | {"provenance":"001.jsonl.gz:6"} | null | "\\section{Limitations and Conclusion}\n\\label{sec:conclusion}\n\nA major limitation of NeRF-SR{} i(...TRUNCATED) | null | null | null | proofpile-arXiv_065-5 | "{\"arxiv_id\":\"2112.01759\",\"language\":\"en\",\"timestamp\":1658456585000,\"url\":\"https:\\/\\/(...TRUNCATED) | 2024-02-18T23:39:39.785Z | 2022-07-22T02:23:05.000Z | null | {"paloma_paragraphs":[]} | null |
null | null | {"provenance":"001.jsonl.gz:7"} | null | "\\section{Introduction}\n\nMachine Learning (ML) applications recently demonstrated widespread adop(...TRUNCATED) | null | null | null | proofpile-arXiv_065-6 | "{\"arxiv_id\":\"2112.01777\",\"language\":\"en\",\"timestamp\":1638756943000,\"url\":\"https:\\/\\/(...TRUNCATED) | 2024-02-18T23:39:39.787Z | 2021-12-06T02:15:43.000Z | null | {"paloma_paragraphs":[]} | null |
null | null | {"provenance":"001.jsonl.gz:8"} | null | "\\section{Introduction}\nSurface codes are an important class of error correcting codes in fault to(...TRUNCATED) | null | null | null | proofpile-arXiv_065-7 | "{\"arxiv_id\":\"2112.01752\",\"language\":\"en\",\"timestamp\":1640657518000,\"url\":\"https:\\/\\/(...TRUNCATED) | 2024-02-18T23:39:39.790Z | 2021-12-28T02:11:58.000Z | null | {"paloma_paragraphs":[]} | null |
null | null | {"provenance":"001.jsonl.gz:9"} | null | "\\section{Introduction}\n\\label{sec:intro}\nThere are numerous links between probabilistic cellula(...TRUNCATED) | null | null | null | proofpile-arXiv_065-8 | "{\"arxiv_id\":\"2112.01778\",\"language\":\"en\",\"timestamp\":1648520388000,\"url\":\"https:\\/\\/(...TRUNCATED) | 2024-02-18T23:39:39.793Z | 2022-03-29T02:19:48.000Z | null | {"paloma_paragraphs":[]} | null |
null | null | {"provenance":"001.jsonl.gz:10"} | null | "\\section{Introduction} \\label{intro}}\n\n\\IEEEPARstart{F}{ace} detection, one of the most popula(...TRUNCATED) | null | null | null | proofpile-arXiv_065-9 | "{\"arxiv_id\":\"2112.01787\",\"language\":\"en\",\"timestamp\":1638756961000,\"url\":\"https:\\/\\/(...TRUNCATED) | 2024-02-18T23:39:39.796Z | 2021-12-06T02:16:01.000Z | null | {"paloma_paragraphs":[]} | null |
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OLMo 2 (November 2024) Pretraining set
Collection of data used to train OLMo-2-1124 models. The majority of this dataset comes from DCLM-Baseline with no additional filtering, but we provide the explicit breakdowns below.
| Name | Tokens | Bytes (uncompressed) | Documents | License |
|---|---|---|---|---|
| DCLM-Baseline | 3.70T | 21.3TB | 2.95B | CC-BY-4.0 |
| Arxiv | 20.8B | 77.2GB | 3.95M | ODC-BY |
| pes2o | 58.6B | 412GB | 38M | ODC-BY |
| starcoder | 83.0B | 458GB | 78.7M | ODC-BY |
| Algebraic-stack | 11.8B | 44.0GB | 2.83M | ODC-BY |
| OpenWebMath | 12.2B | 47.23GB | 2.89M | ODC-BY |
| Wiki | 3.66B | 18.1GB | 6.17M | ODC-BY |
| Total | 3.90T | 22.4TB | 3.08B | ODC-BY |
Please refer to the OLMo2 Tech Report for further details.
Licensing Information
This collection is released under the Open Data Commons Attribution License (ODC-By) v1.0 license. The use of this dataset is also subject to CommonCrawl's Terms of Use.
Citation
A technical manuscript is forthcoming!
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