Gemma: Open Models Based on Gemini Research and Technology

• URL: http://arxiv.org/abs/2403.08295v4
• Date: Tue, 16 Apr 2024 12:52:47 GMT
• Title: Gemma: Open Models Based on Gemini Research and Technology
• Authors: Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Léonard Hussenot, Pier Giuseppe Sessa, Aakanksha Chowdhery, Adam Roberts, Aditya Barua, Alex Botev, Alex Castro-Ros, Ambrose Slone, Amélie Héliou, Andrea Tacchetti, Anna Bulanova, Antonia Paterson, Beth Tsai, Bobak Shahriari, Charline Le Lan, Christopher A. Choquette-Choo, Clément Crepy, Daniel Cer, Daphne Ippolito, David Reid, Elena Buchatskaya, Eric Ni, Eric Noland, Geng Yan, George Tucker, George-Christian Muraru, Grigory Rozhdestvenskiy, Henryk Michalewski, Ian Tenney, Ivan Grishchenko, Jacob Austin, James Keeling, Jane Labanowski, Jean-Baptiste Lespiau, Jeff Stanway, Jenny Brennan, Jeremy Chen, Johan Ferret, Justin Chiu, Justin Mao-Jones, Katherine Lee, Kathy Yu, Katie Millican, Lars Lowe Sjoesund, Lisa Lee, Lucas Dixon, Machel Reid, Maciej Mikuła, Mateo Wirth, Michael Sharman, Nikolai Chinaev, Nithum Thain, Olivier Bachem, Oscar Chang, Oscar Wahltinez, Paige Bailey, Paul Michel, Petko Yotov, Rahma Chaabouni, Ramona Comanescu, Reena Jana, Rohan Anil, Ross McIlroy, Ruibo Liu, Ryan Mullins, Samuel L Smith, Sebastian Borgeaud, Sertan Girgin, Sholto Douglas, Shree Pandya, Siamak Shakeri, Soham De, Ted Klimenko, Tom Hennigan, Vlad Feinberg, Wojciech Stokowiec, Yu-hui Chen, Zafarali Ahmed, Zhitao Gong, Tris Warkentin, Ludovic Peran, Minh Giang, Clément Farabet, Oriol Vinyals, Jeff Dean, Koray Kavukcuoglu, Demis Hassabis, Zoubin Ghahramani, Douglas Eck, Joelle Barral, Fernando Pereira, Eli Collins, Armand Joulin, Noah Fiedel, Evan Senter, Alek Andreev, Kathleen Kenealy,
• Abstract summary: This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate strong performance across academic benchmarks for language understanding, reasoning, and safety.
• Score: 128.57714343844074
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work introduces Gemma, a family of lightweight, state-of-the art open models built from the research and technology used to create Gemini models. Gemma models demonstrate strong performance across academic benchmarks for language understanding, reasoning, and safety. We release two sizes of models (2 billion and 7 billion parameters), and provide both pretrained and fine-tuned checkpoints. Gemma outperforms similarly sized open models on 11 out of 18 text-based tasks, and we present comprehensive evaluations of safety and responsibility aspects of the models, alongside a detailed description of model development. We believe the responsible release of LLMs is critical for improving the safety of frontier models, and for enabling the next wave of LLM innovations.

Related papers

• xGen-MM (BLIP-3): A Family of Open Large Multimodal Models [157.44696790158784]
  This report introduces xGen-MM, a framework for developing Large Multimodal Models (LMMs) The framework comprises meticulously curated datasets, a training recipe, model architectures, and a resulting suite of LMMs. Our models undergo rigorous evaluation across a range of tasks, including both single and multi-image benchmarks.
  arXiv  Detail & Related papers  (2024-08-16T17:57:01Z)
• Gemma 2: Improving Open Language Models at a Practical Size [118.04200128754249]
  We introduce Gemma 2, a new addition to the Gemma family of lightweight, state-of-the-art open models. We apply several known technical modifications to the Transformer architecture, such as interleaving local-global attentions. The resulting models deliver the best performance for their size, and even offer competitive alternatives to models that are 2-3 times bigger.
  arXiv  Detail & Related papers  (2024-07-31T19:13:07Z)
• ShieldGemma: Generative AI Content Moderation Based on Gemma [49.91147965876678]
  ShieldGemma is a suite of safety content moderation models built upon Gemma2. Models provide robust, state-of-the-art predictions of safety risks across key harm types.
  arXiv  Detail & Related papers  (2024-07-31T17:48:14Z)
• Large Language Model Pruning [0.0]
  We suggest a model pruning technique specifically focused on LLMs. The proposed methodology emphasizes the explainability of deep learning models. We also explore the difference between pruning on large-scale models vs. pruning on small-scale models.
  arXiv  Detail & Related papers  (2024-05-24T18:22:15Z)
• Benchmarking Llama2, Mistral, Gemma and GPT for Factuality, Toxicity, Bias and Propensity for Hallucinations [0.0]
  This paper introduces fourteen novel datasets for the evaluation of Large Language Models' safety in the context of enterprise tasks. A method was devised to evaluate a model's safety, as determined by its ability to follow instructions and output factual, unbiased, grounded, and appropriate content.
  arXiv  Detail & Related papers  (2024-04-15T13:40:08Z)
• Have You Merged My Model? On The Robustness of Large Language Model IP Protection Methods Against Model Merging [25.327483618051378]
  We conduct the first study on the robustness of IP protection methods under model merging scenarios. Experimental results indicate that current Large Language Model (LLM) watermarking techniques cannot survive in the merged models. Our research aims to highlight that model merging should be an indispensable consideration in the robustness assessment of model IP protection techniques.
  arXiv  Detail & Related papers  (2024-04-08T04:30:33Z)
• LLaVA-Gemma: Accelerating Multimodal Foundation Models with a Compact Language Model [4.6373877301731]
  We train a suite of multimodal foundation models (MMFM) using the popular LLaVA framework with the recently released Gemma family of large language models (LLMs) We test the effect of ablating three design features: pretraining the connector, utilizing a more powerful image backbone, and increasing the size of the language backbone. The resulting models, which we call LLaVA-Gemma, exhibit moderate performance on an array of evaluations, but fail to improve past the current comparably sized SOTA models.
  arXiv  Detail & Related papers  (2024-03-29T21:32:50Z)
• Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning [52.29522018586365]
  We study structured pruning as an effective means to develop smaller LLMs from pre-trained, larger models. Our approach employs two key techniques: (1) targeted structured pruning, which prunes a larger model to a specified target shape by removing layers, heads, and intermediate and hidden dimensions in an end-to-end manner, and (2) dynamic batch loading, which dynamically updates the composition of sampled data in each training batch based on varying losses across different domains.
  arXiv  Detail & Related papers  (2023-10-10T15:13:30Z)
• Llama 2: Open Foundation and Fine-Tuned Chat Models [65.43397761706336]
  We develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases.
  arXiv  Detail & Related papers  (2023-07-18T14:31:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.