The Filecoin Virtual Machine unlocks boundless possibilities for innovation on the Filecoin network. Here are many ideas for systems, apps, and building blocks we’d like to see built. We are counting on you to capture these opportunities, and turn these ideas into reality!
This RFS also includes a star guide in order to call out specific items we want to see more of: watch out for 🌟 to see startups and tools we want to see prioritized. The more 🌟 the more we want to see this!
Version: v1.6
Date: 2024-02-02
<aside> 🌱 Editor notes
Last updated on Feb 2, 2024
We continue to have a lot of interest here, especially after the release of open source models like Llama 2 and Code Llama! We also have partner projects (like Lilypad and Fluence) working on similar tools!
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FVM is in a unique spot to contribute to the recent transformer revolution with OpenAI and its competitors. There are centralized catalogues of these models on sites like HuggingFace, which let you interact with the models directly from your browser.
FVM provides an ability to decentralize the storage of model training data and the models themselves. (See more here.)
Consider how powerful it could be to create better weights and biases on an LLM, based on an objective evaluation set (such as Human Eval). With the right incentives, participants could bootstrap from a known open source model, and continue to increase training and fine-tuning to the point that it starts to outperform known hosted models. The future of LLMs can be decentralized, open and more performant than proprietary LLM solutions with the right incentives.
More broadly, we can also incentivize the ability to augment these models in new and interesting ways. This can happen through rewarding the creation or collection of new training data for these models, new model architectures, and new inference tools for large language models. Each of these developments can allow for data organizations to provide tokens to contributors, testers and dataset curators.
A truly open solution to large language models will allow them to be used, developed and retrained by the members of the organizations that support them. This creates a compelling future for the transformer revolution — one that places the open source community at the center of future ML advances. It also serves as a useful competitor to the prevailing market approach — that of data collected by large organizations to train proprietary, closed-source models.
<aside> 🌱 Editor notes
Last updated on May 8, 2023
We are interested in teams that can onboard lots of large video and VR files to the network. We want teams here that can focus on the end user experience with uploading, viewing, and experiencing large video / VR assets. The business development work and marketing needed here is key to making a successful video storage onramp.
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Filecoin needs to take advantage of unused storage capability that the network currently holds. It can do so through onramps for a variety of storage needs and data organizations that can incentivize the upload of more data onto the network.