Summary of H2o-danube-1.8b Technical Report, by Philipp Singer et al.
H2O-Danube-1.8B Technical Report
by Philipp Singer, Pascal Pfeiffer, Yauhen Babakhin, Maximilian Jeblick, Nischay Dhankhar, Gabor Fodor, Sri Satish Ambati
First submitted to arxiv on: 30 Jan 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 This paper introduces H2O-Danube, a series of small language models that achieve highly competitive metrics across various benchmarks. The models, consisting of H2O-Danube-1.8B and its incremental improved version H2O-Danube2-1.8B, are trained on large datasets and follow core principles from LLama 2 and Mistral. The paper also explores techniques for pre-training large language models and releases chat models trained with supervised fine-tuning and direct preference optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a series of small language models called H2O-Danube that did well on many tests. They made the models better by adding more training data, which is like learning from more teachers. The new model is the best one in its size range right now, according to a leaderboard. The team also shared special chat models that can understand what people want and respond accordingly. |
Keywords
* Artificial intelligence * Fine tuning * Llama * Optimization * Supervised
