Completed

Soubir Acharya

Project Owner

Automated HuggingFace to SNET Pipeline

Project for Round 2
Funding Awarded $75,000 USD
Funding Pools New projects
Milestones 7 / 7 Completed

Status

Overall Status
🥳 Completed & Paid
Funding Transfered
$75,000_USD
Max Funding Amount
$75,000_USD

Project Tags:
Algorithmic/technical

Funding Schedule

View Milestones

Milestone Release 1	$9,000 USD	Transfer Complete	TBD
Milestone Release 2	$9,000 USD	Transfer Complete	TBD
Milestone Release 3	$9,000 USD	Transfer Complete	TBD
Milestone Release 4	$9,000 USD	Transfer Complete	TBD
Milestone Release 5	$9,000 USD	Transfer Complete	TBD
Milestone Release 6	$11,250 USD	Transfer Complete	TBD
Milestone Release 7	$18,750 USD	Transfer Complete	15 Mar 2024

Overview

HuggingFace is where a lot of AI models and applications live today. We propose a UI driven fully automated conversion of these applications and models to SingularityNet services. AI developers can transition from a limited Dev experience to production ready Web3 enabled AI service that they can share with the world and get paid for.

Proposal Description

AI services (New or Existing)

Compnay Name

Zero2AI

Service Details

1. Service(s) that will be developed and onboarded as part of this project:
Web portal - For automatic conversion of a HuggingFace model or space to a SNET service + Dapp

2. Existing services on our marketplace that will be utilized as a part of this project:
SNET Python SDK

Problem Description

The goal is to connect the HuggingFace ecosystem (the pure DEV AI world) to the Web3 SingularityNet world. Today bringing existing or new services to SNET is hard. The learning curve is steep, the tooling is too low level the time to port to SNET is high. On the Dev and Web2 side getting compensated for one's efforts is not easy. But it need not be!

Solution Description

We propose a UI-driven fully automated conversion of all HuggingFace spaces and models to SingularityNet. The User Experience is simply to point to a model card or a space and answer a few questions. Push a button. SNET service will be created and run in a container in a sandbox. Test and confirm functionality. Once satisfied push a button to Publish on Mainnet. The service is then live including a Gradio or Streamlit frontend. This is the ultimate No-code experience.

Deliverables

Demo basic conversion - week 3

Demo a popular Gradio space - week 6

Demo a popular StreamLit space - week 9

Demo a popular Docker space - week 12

Zero2AI smartphone app demo - week 15

Public Release with GPU backend - week 18

Revenue Sharing

We do have plans of launching a community token for incentivizing both developers and consumers and intend to keep a set aside for the treasury. But beyond that, the success of this No-code automated onboarding of SOTA AI solutions from the leading AI hub HuggingFace could potentially change the SNET marketplace landscape with hundreds of new solutions almost overnight.

Marketing & Competition

We believe even though HuggingFace is a very rich ecosystem for AI experimentation, a big gap exists when it comes to the monetization of applications. Connecting HuggingFace to SNET will enable AI developers to participate in the AI economy. Our audience is AI developers and enthusiasts. We plan to reach them via Hacker News, YouTube promotion, and Reddit posts. We do not see much competition in the automatic transformation from pure Web2 to Web3 solutions. Web3 AI marketplaces are still early in our opinion and suffer from not enough awareness and a lack of tooling.

Needed Resources

We would love the community's feedback in fine-tuning our requirements and soon-to-test and on-board solutions that exist on HuggingFace. Additionally evangelizing the solution and benefits of being on SingularityNet to the HuggingFace community would be a tremendous help.

AI Services

Activity Summary

Milestones

7

Total

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Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses. Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively). Feasibility (secondary) This represents the user's assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding. Viability (secondary) This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?” Examples:

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Desirability (secondary) Even if the project team succeeds in creating a product, there is the question of market fit. Is this a project that fulfills an actual need? Is there a lot of competition already? Are the USPs of the project sufficient to make a difference? Example:

Creating a translation service from, say Spanish to English might be possible, but it's questionable if such a service would be able to get a significant share of the market

Usefulness (secondary) This is a crucial category that aligns with the main goal of the Deep Funding program. The question to be asked here is: “To what extent will this proposal help to grow the Decentralized AI Platform?” For proposals that develop or utilize an AI service on the platform, the question could be “How many API calls do we expect it to generate” (and how important / high-valued are these calls?). For a marketing proposal, the question could be “How large and well-aligned is the target audience?” Another question is related to how the budget is spent. Are the funds mainly used for value creation for the platform or on other things? Examples:

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses. Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively). Feasibility (secondary) This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding. Viability (secondary) This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?” Examples:

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
A proposal that promises a new Carbon Emission Compensation scheme might be technically feasible, but the viability could be estimated low due to challenges around market penetration and widespread adoption.

Creating a translation service from, say Spanish to English might be possible, but it\'s questionable if such a service would be able to get a significant share of the market

A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

Total Milestones
7
Total Budget
$75,000_USD
Last Updated
27 May 2025

Milestone 1 - StreamLit public app

Status

😀 Completed

Description

UI for Hugging Face Spaces Discovery+Simple Text2Text apps