GPT-4, without specialized training, beat a GPT-3.5 class model that cost $10B

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  • FinGPT

    FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.

  • There is also the open source FinGPT, that is claimed to beat GPT4 in some benchmarks at a fine tuning cost of $17.25.

    https://github.com/AI4Finance-Foundation/FinGPT

  • OpenPipe

    Turn expensive prompts into cheap fine-tuned models

  • IMO it's possible to over-generalize from this datapoint (lol). While it's true that creating a general "finance" model that's stronger than GPT-4 is hard, training a task-specific model is much easier. Eg. "a model that's better than GPT-4 at answering finance-related questions": very hard. "A model that's better than GPT-4 at extracting forward-looking financial projections in a standard format": very easy.

    And in practice, most tasks people are using GPT-4 for in production are more like the latter than the former.

    (Disclaimer: building https://openpipe.ai, which makes it super easy to productize this workflow).

  • InfluxDB

    Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.

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  • ok-robot

    An open, modular framework for zero-shot, language conditioned pick-and-drop tasks in arbitrary homes.

  • Thanks! Appreciate the kind words. I should have in the next month or so (interviewing and finishing my Master's, so there's been delays) a follow up that follows more advancements in the router style VLA, sensoiromotor VLM, and advances in embedding enriched vision models in general.

    If you want a great overview of what a modern robotics stack would look like with all this, https://ok-robot.github.io/ was really good and will likely make it into the article. It's a VLA combined with existing RL methods to demonstrate multi-tasking robots, and serves as a great glimpes into what a lot of researchers are working on. You won't see these techniques in robots in industrial or commercial settings - we're still too new at this to be reliable or capable enough to deploy these on real tasks.

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