Understanding LLMs is key to unlocking AI's potential! This infographic breaks down LLM sizes (Small to Very Large) and their use cases, from foundation models to fine-tuned chats. How will LLMs shape your industry? #AI #LLM #TechInnovation Follow @ingliguori for more
LLMS
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Databricks Partners with Meta to Launch Llama 3.2 Models
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We recently partnered with Meta to launch Llama 3.2 models on Databricks!
— Databricks (@databricks) 17 octobre 2024
You can now tune them securely on your data, and easily integrate them into your GenAI applications with Mosaic AI Gateway and Agent Framework:https://t.co/tW0YmCotRy pic.twitter.com/mmMiQVnsJAWe recently partnered with Meta to launch Llama 3.2 models on Databricks! You can now tune them securely on your data, and easily integrate them into your GenAI applications with Mosaic AI Gateway and Agent Framework: https://
dbricks.co/48gmche -
Nemotron: Nvidia’s Model and Benchmark Evaluation Issues
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To be clear: imo, Nemotron is a good model and a valuable contribution. Nvidia was factual in their claims and we shouldn't overhype it. If this work reveals issues with these benchmarks, it's even better for the community.
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Diffusion Forcing: Versatile Sequence Model Backbone for World Models
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Across each demo, Diffusion Forcing acted as a full sequence model, a next-token prediction model, or both. According to the researchers, this versatile approach could potentially serve as a powerful backbone for a "world model" one day.
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Diffusion Forcing: Neural Networks Learn Token Denoising and Prediction
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Diffusion Forcing trains neural networks to cleanse a collection of tokens, removing different amounts of noise w/i each one while simultaneously predicting the next few tokens.
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MIT’s Diffusion Forcing: Combining Token Prediction and Video Diffusion
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Sequence models have skyrocketed in popularity for their ability to analyze data & predict what to do next.
— MIT CSAIL (@MIT_CSAIL) 17 octobre 2024
MIT’s "Diffusion Forcing" method combines the strengths of next-token prediction (like w/ChatGPT) & video diffusion (like w/Sora), training neural networks to handle… pic.twitter.com/u9CPIGq0tjSequence models have skyrocketed in popularity for their ability to analyze data & predict what to do next. MIT’s "Diffusion Forcing" method combines the strengths of next-token prediction (like w/ChatGPT) & video diffusion (like w/Sora), training neural networks to handle
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Honest Model Evaluation Over Hype in AI Research
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This is the opposite imo: they claimed good scores on three benchmarks and that's it. No grandiose claims in the model card or the paper. The team made a valuable contribution, let's just not overhype it.
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Llama 3.1 Nemotron reveals benchmark evaluation disparities
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Llama-3.1-Nemotron-70B is a good reminder that chat capabilities evaluated by Arena Hard, AlpacaEval, and MT-Bench correlate poorly with benchmarks like MMLU and GPQA. They also provide a useful but narrow view of human preferences. Blame the benchmarks, not the models
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Meta Llama 3.2: Fast Inference AI Development Now Free
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The possibilities are endless with @AIatMeta
's Llama 3.2! With so many potential use cases out there, this is the perfect time to get creative and build a valuable #AI with hyper-fast #inference. Start developing for free -
Glasses unmask AI: Scammer realizes she’s talking to ChatGPT.
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Il suffit de parler de lunettes pour démasquer une IA ?
— Defend Intelligence (Anis Ayari) (@DFintelligence) 17 octobre 2024
Dans cette deuxième partie, la démarcheuse téléphonique se rend compte qu’elle parle à ChatGPT à la suite d’un message de modération et essaye de lui faire avouer…
OpenAI a mis tout un tas de garde-fous (parfois ça en… pic.twitter.com/68iFtEc4oTJust talking about glasses is enough to unmask an AI? In this second part, the phone scammer realizes she's talking to ChatGPT following a moderation message and tries to get it to admit it… OpenAI has put in place a whole bunch of safeguards (sometimes it even becomes