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The Model of models

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35,254 просмотров • 15 дней назад •via X (Twitter)

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Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.
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Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

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Apple's AI Reasoning Model Paper Explained Apple’s disruptive new AI reasoning model research paper, “The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models” explained, and what it means for AGI.
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Apple's AI Reasoning Model Paper Explained Apple’s disruptive new AI reasoning model research paper, “The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models” explained, and what it means for AGI.

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Introducing Decagon Labs. The research team behind the models and infrastructure powering 80% of Decagon’s model traffic. More in the thread. ↓
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New version of The Models Resource just dropped adding an awesome 3D model viewer right into the website so you can now preview models without needing to download them!
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THE FUTURE OF AI APPLICATIONS Jensen Huang: “Another breakthrough happened… the first time I realized Perplexity was using multiple models at the same time, I thought it was completely genius.” The future isn’t one great model… It’s orchestrating multiple great models at every step of the reasoning chain. Multi-model. Multimodal. Multi-cloud.

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“Reasoning has fundamentally changed the economics of these frontier models.” Gavin Baker says reasoning lets scaled user bases unlock a new flywheel. Users improve the model, and the model improves the product. Gavin Baker
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a16z

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There are hundreds of AI models, but no single model is best at everything. DGrid AI's AI Arena lets 300,000+ users blind-test model responses and vote for the better one, helping map which models perform best for different tasks.
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"When Deepseek models came out, and Kimi models and others, those are the predominant models in the world.... so I think there absolutely is an appetite for the cheaper, you know, sort of Prius level model versus, versus the Ferrari out there" -Naveen Rao
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"When Deepseek models came out, and Kimi models and others, those are the predominant models in the world.... so I think there absolutely is an appetite for the cheaper, you know, sort of Prius level model versus, versus the Ferrari out there" -Naveen Rao

Deirdre Bosa

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“AI MODELS COULD BECOME 10× SMALLER WITHOUT LOSING MOST OF THEIR INTELLIGENCE.” Mark Zuckerberg says one of the most powerful developments in AI is model distillation: taking the capabilities of a much larger model and transferring them into a much smaller one. - Meta's huge frontier models can serve as "teachers" for smaller models. - Zuckerberg says a model could potentially retain around 90–95% of the larger model's intelligence while being only ~10% of its size. - That means dramatically lower inference costs, memory requirements and computing power. - Developers can distill and customize these capabilities for their own applications. And that's where open-source models come into the picture.
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Can we extend the power of world models beyond just online model-based learning? Absolutely! We believe the true potential of world models lies in enabling agents to reason at test time. Introducing DINO-WM: World Models on Pre-trained Visual Features for Zero-shot Planning.

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Scott Adams: If climate models were science, there'd be 1, not 36 failures shredded by the DOE report, and panels of model-makers on CNN/MSNBC rushing to defend the models. Scott Adams
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Which model of the 40 models is the right model? The CSIRO can’t say. They just want us to trust them. Apparently they want us to believe that 40 different models can all come to the same conclusion. The only same conclusion I can see is that the taxpayer is ripped off. #auspol
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Which model of the 40 models is the right model? The CSIRO can’t say. They just want us to trust them. Apparently they want us to believe that 40 different models can all come to the same conclusion. The only same conclusion I can see is that the taxpayer is ripped off. #auspol

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