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Research And Development
R&D Overview
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Advancing Generative AI through Innovation
The R&D team at LightOn plays a pivotal role in advancing the field of generative AI through continuous innovation and development. Their expertise spans across creating and fine-tuning large language models (LLMs) that form the backbone of the Paradigm platform, a comprehensive AI solution designed for enterprise use. This platform simplifies the integration of generative AI into business workflows, offering both on-premise and cloud options to ensure flexibility and scalability for various business needs
Pioneering AI with Alfred-40B-0723
One of the key achievements of LightOn's R&D team is the development of Alfred-40B-0723, an open-source LLM based on Falcon-40B. This model is fine-tuned using reinforcement learning from human feedback, enhancing its ability to perform complex tasks such as content summarization, query answering, and prompt engineering. The team's ongoing efforts ensure that Alfred remains at the cutting edge of AI technology, providing robust support for the Paradigm platform and enabling enterprises to deploy AI solutions that are secure, scalable, and tailored to their specific requirements
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Recent Posts
LightOn Pulse #2: Discover the New Paradigm Platform Updates
Version Glorious-Giraffe 20240828 Release Date: August 28, 2024
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PyLate: Flexible Training and Retrieval for ColBERT Models
We release PyLate, a new user-friendly library for training and experimenting with ColBERT models, a family of models that exhibit strong retrieval capabilities on out-of-domain data.
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Training Mamba Models on AMD MI250/MI250X GPUs with Custom Kernels
In this blogpost we show how we can train a Mamba model interchangeably on both NVIDIA and AMD and we compare both training performance and convergence in both cases. This shows that our training stack is becoming more GPU-agnostic.
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Transforming LLMs into Agents for Enterprise Automation
Developing Agentic Capabilities for LLMs to automate business workflows and create smart assistants.
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Passing the Torch: Training a Mamba Model for Smooth Handover
We present our explorations on training language models based on the new Mamba architecture, which deviates from the traditional Transformer architecture.
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