Give it a try again with the latest checkpoint, it's a great model.
GENERATIVE AI
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Scaling Laws for Fact Memorization in Large Language Models
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Scaling Laws for Fact Memorization of Large Language Models this paper is from last year (2024); it's a very well-executed study of the total number of facts a model can memorize they estimate 1T parameters are needed to perfectly memorize all relational pairs in wikipedia
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Knowledge Storage Capacity in Language Model Parameters
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How Much Knowledge Can You Pack Into the Parameters of a Language Model? enter, transformers- in 2020 these folks had a different idea: once "pretraining" is a thing, you can measure the amount of 'facts' that the model 'knows', without training it at all
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Capacity Measurements for Language Models Research
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in prep for our new research dropping on ArXiv tomorrow (i think), here is a thread about…. CAPACITY MEASUREMENTS FOR LANGUAGE MODELS
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Average of Five Top AI Models You Talk With Most
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You are the average of the 5 people you talk to the most: Opus
Sonnet
o3
Gemini Pro
DeepSeek R1 -
Claude 3.5 Availability: A Game-Changer for AI Adoption
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Till 3.5 comes, it basically doesn’t exist for me.
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Gemini API improvements and model quality assessment
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I love Gemini, for the record 🙂 They only recently fixed their caching situation, and their API is still too complicated, but the models are great.
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Career Future Prospects in AI and Technology
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Do you feel excited about the possibilities of your career future?
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Discover Magicpathai: Next-Generation AI Tool Worth Trying
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Try @Magicpathai and thank me later!
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LLM Coding Capabilities: From GPT-2 to GPT-4o Progress
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> 2020: gpt2 can't write code
> 2021: gpt3 can't reliably write python
> 2022: instructgpt can't write blocks of code without syntax errors
> 2023: chatgpt can't do leetcode
> 2024: gpt4 can't debug CUDA
> 2025: gpt4o can't implement entire PR these LLMs are not to be trusted…