TheSequence - Gemma 2: A Release That Matters
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You can subscribe to The Sequence below:📝 Editorial: Gemma 2: A Release That MattersSmall language models (SLMs) are gaining a lot of momentum in both research and product releases. SLMs have the potential to streamline the adoption of generative AI and unlock use cases such as on-device AI. Most of the SLM releases from the past year have been limited to the models themselves, and that’s hardly enough. Adopting SLMs (just like larger models) comes with significant challenges. Interpretability and security certainly rank at the top of the list. Last week, Google took a major step in addressing some of these challenges with a new release of its Gemma stack. In June, Google announced the release of Gemma 2, a series of mid-size models with 9 billion and 27 billion parameters, respectively. A few days ago, Google extended that release with some exciting additions:
The Gemma release is significant because it is open source. This should encourage researchers and developers to expand on these capabilities, particularly in the case of Gemma Scope, given that interpretability is such a challenge in generative AI applications. I have been tinkering with Gemma Scope over the past few days, and it’s pretty useful. With this release, it seems that Gemma is evolving from a family of models to complete stacks to enable SLM adoption in real-world applications. This release definitely matters. What’s the best model for RAG? Our latest LLM Hallucination Index ranks 22 of the leading models on their performance across 3 different RAG tasks, evaluating the correctness of their responses and propensity to hallucinate. See which model comes out on top and why larger is not always better…" 🔎 ML ResearchGemma 2Google DeepMind published a paper detailing Gemma 2, a new set of small models ranging from 2 to 27 billion parameters. Gemma 2 extends its predecessors with new techniques such as interleaving local-global attentions and group query attention —> Read more. SAM 2Meta AI published a paper introducing the second version of its Segment Anything Model(SAM) for real time object segmentation. SAM expands over its predecesors by providing an unified model for object segmentation in videos and images —> Read more. TraceMicrosoft Research published a paper and open source code of Trace, a framework for AI systems optimization. Trace is a new AutoDiff like tool but can be used in systems without gradients —> Read more. CMU-MATHCarnegie Mellon University researchers published details about CMU-MATH, a model that took second place in the AI mathematical olympiad. CMU-MATH uses a dual model system that which includes a policy model that produces multiple solutions and a reward model that chooses the answer with the highest weight —> Read more. MoMaMeta AI publishd a paper introducing MoMa, a mixture-of-experts architecture for mixed model models. MoMa is based on the Chameleon architecture and processes images and text in arbitrary sequences —> Read more. Berkeley HumanoidAI researchers from UC Berkeley published a paper introducing Berkeley Humanoid, a research framework for learning based control. The framework includes a robot designed to learn algorithm with low simulation complexity —> Read more. 🤖 AI Tech ReleasesGemma 2Google open sourced new additions to its Gemma models including a 2B parameter model, a guardrail framework and an interpretability tool —> Read more. torchchatThe PyTorch team released torchchat, a library for accelerated inference in laptop, mobile and desktop devices —> Read more. Stable Fast 3DStability AI open sourced Stable Fast 3D, a model for rapid 3D asset generation —> Read more. 🛠 Real World AIAgents in the EnterpriseSalesforce shares some perspectives about the impact of AI agents in enterprise automation tasks —> Read more. 📡AI Radar
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Gemma 2: A Release That Matters
Sunday, August 4, 2024
A new model, a guardrails framework and an interpretability tool. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
📽 [Webinar] Beat GPT-4 with a Small Model and 10 Rows of Data*
Friday, August 2, 2024
Small language models (SLMs) are increasingly rivaling the performance of large foundation models like GPT-4. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Edge 418: Meet The New DSPy: The Hot Framework to Build LLM Apps You Should Know About
Thursday, August 1, 2024
Created by Stanford researchers, the framework provides an interesting alternative to LangChain or LlamaIndex. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
Edge 417: Building Multi Agent Systems
Tuesday, July 30, 2024
Can multiple agents collaborate in a single application? Some research and technologies that might help with that. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
3 vs. 3: The Open vs. Closed Battle for Big AI
Sunday, July 28, 2024
Big model announcements by Meta, Mistral and xAI. ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏ ͏
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