How to Build Responsible AI in the Era of Generative AI?: Introduction We now live in the age of artificial intelligence, where everything around… https://
analyticsvidhya.com/blog/2024/09/r
esponsible-generative-ai/?utm_source=dlvr.it&utm_medium=twitter
… #DataAnalytics #DataScience #DataDriven #DeepLearning #BusinessIntelligence #Blockchain #Infrastructure #AI
GENERATIVE AI
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Building Responsible AI in the Generative AI Era
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SFT vs DPO: Finding the Sweet Spot in Model Training
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In my experience, SFT is quite destructive, which is why I like DPO better. There might be a sweet spot with SFT and low LRs though. I haven't experimented with it that much tbh.
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Adding Models to Hugging Face Hub Platform
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Looks cool! Would be awesome to add them to http://
hf.co/models! -
Testing Liquid AI Foundation Models Across Multiple Platforms
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You can test LFMs today using the following links:
– Liquid AI Playground: https://
playground.liquid.ai
– Lambda: https://
lambda.chat/chatui/models/
/models/LiquidCloud
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– Perplexity: https://
labs.perplexity.ai If you're interested, find more information in our blog post: -
LFM Architecture Enables New Foundation Model Design Space
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The LFM architecture opens a new design space for foundation models. This is not restricted to language, but can be applied to other modalities: audio, time series, images, etc. It can also be optimized for specific platforms, like @Apple
, @AMD
, @Qualcomm
, and @cerebras -
LFM Architecture: Memory-Efficient LLM for Long Contexts
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The LFM architecture is also super memory efficient. While the KV cache in transformer-based LLMs explodes with long contexts, we keep it minimal, even with 1M tokens. This unlocks new applications, like document and book analysis, directly in your browser or on your phone.
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Liquid AI Launches Three LLMs with SOTA Performance and Edge Optimization
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This is the proudest release of my career 🙂 At @liquidai
, we're launching three LLMs (1B, 3B, 40B MoE) with SOTA performance, based on a custom architecture. Minimal memory footprint & efficient inference bring long context tasks to edge devices for the first time! -
LFM optimization outperforms transformers at 1B to 40B scale
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We optimized LFMs to maximize knowledge capacity and multi-step reasoning. As a result, our 1B and 3B models significantly outperform transformer-based models in various benchmarks. And it scales: our 40B MoE (12B activated) is competitive with much bigger dense or MoE models.
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Implementing Multimodal Models with Hugging Face Transformers
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Implementing Multimodal Models with Hugging Face Transformers: Learn to use the advanced models from Hugging Face. https://
kdnuggets.com/implementing-m
ultimodal-models-with-hugging-face-transformers?utm_source=dlvr.it&utm_medium=twitter&utm_campaign=implementing-multimodal-models-with-hugging-face-transformers
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