Summary of H2o-danube3 Technical Report, by Pascal Pfeiffer et al.
H2O-Danube3 Technical Report
by Pascal Pfeiffer, Philipp Singer, Yauhen Babakhin, Gabor Fodor, Nischay Dhankhar, Sri Satish Ambati
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed H2O-Danube3 series of small language models comprises two variants: H2O-Danube3-4B, pre-trained on 6T tokens, and H2O-Danube3-500M, pre-trained on 4T tokens. These models are initially trained on high-quality Web data in three stages with varying data mixes before fine-tuning for chat applications. The models demonstrate highly competitive performance across academic, chat, and fine-tuning benchmarks. Notably, the compact architecture of H2O-Danube3 enables efficient processing on modern smartphones, allowing for local inference and rapid processing capabilities even on mobile devices. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The H2O-Danube3 series is a set of small language models designed to be efficient and effective. These models are trained on large amounts of Web data and can perform well in chat applications. They’re also very fast and can run on smartphones, which makes them useful for people who want to use AI-powered chatbots on their mobile devices. |
Keywords
» Artificial intelligence » Fine tuning » Inference
