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Openledger
Openledger
Openledger is a blockchain network that supports artificial intelligence (AI) applications by providing a decentralized infrastructure for developing and monetizing specialized AI models. It aims to create an economic framework that facilitates verifiable data attribution and crypto-economic incentives within an AI-driven ecosystem. [11]
Overview
Openledger is a blockchain network that supports AI development by providing a decentralized infrastructure for building and monetizing specialized language models (SLMs). It addresses challenges in AI, such as access to specialized data, transparent attribution, and fair compensation, by integrating blockchain-based economic mechanisms. Openledger uses a layered architecture: datanets for domain-specific data collection, a "Proof of Attribution" system for tracking data influence, and an EVM-compatible Layer 2 network built on the OP Stack with EigenDA for data availability. This setup enables low-cost, scalable deployment of AI models and applications, allowing contributors, developers, and validators to collaborate and be rewarded in a transparent and sustainable AI ecosystem. [1] [2]
Technology
Datanets
Datanets in Openledger are decentralized data networks designed to collect, validate, and distribute specialized datasets for training domain-specific AI models. These networks function as structured, transparent repositories where contributors can provide high-quality data with verifiable attribution. Datanets support a trustless system involving owners, contributors, and validators, ensuring data accuracy and integrity. Specialized data is essential for improving AI models' performance, explainability, and efficiency, tailored to specific domains. Datanets are central in powering specialized AI agents while promoting sustainable, decentralized participation in the data economy by enabling fine-tuned, verifiable, and interpretable model development. [3] [12]
Proof of Attribution
OpenLedger’s Proof of Attribution system establishes a cryptographically secure and transparent method for linking each data contribution to AI model outputs. This mechanism ensures that every dataset used in training can be traced back to its source, recorded immutably on-chain, and assessed for its impact on the model’s behavior. It introduces accountability and trust in AI development by enabling contributors to receive rewards proportional to the value of their data, while discouraging low-quality or malicious inputs through penalty systems.
The attribution process begins when contributors submit domain-specific datasets tagged with metadata and stored within Datanets. These datasets are evaluated for their feature-level influence on training and the contributor's reputation, creating an influence score determining their reward share. These contributions are logged and validated during and after model training, with high-impact data resulting in greater token-based incentives. The contributor faces penalties such as stake slashing or reduced future rewards if data is flagged for redundancy, bias, or adversarial content. Altogether, Proof of Attribution supports a trustless and verifiable data attribution pipeline, incentivizing quality participation while ensuring model integrity and transparency. [4] [5]
RAG Attribution
OpenLedger’s RAG Attribution system integrates Retrieval-Augmented Generation (RAG) with blockchain-based data attribution to ensure that AI-generated outputs are verifiable and reward-aligned. In this framework, every response from an AI model is backed by retrieved data from OpenLedger’s indexed datasets, with each source attributed to its original contributor. This approach improves the accuracy and reliability of model outputs and maintains full data provenance and traceability, reducing the risk of misinformation.
The RAG Attribution pipeline starts when a user submits a query, prompting the model to retrieve relevant data from OpenLedger’s decentralized data reservoirs. Each retrieved information is cryptographically logged, ensuring its use is recorded on-chain. Contributors to these datasets receive micro-rewards based on how often and significantly their data is used in responses. Additionally, the system embeds transparent citations into model outputs, enabling users to verify the origins of the generated content. This structure incentivizes high-quality data contributions while building trust in AI-driven insights. [6]
Products
Model Factory
ModelFactory is OpenLedger’s no-code platform for securely fine-tuning large language models (LLMs) using permissioned datasets. It replaces traditional command-line tools and APIs with a fully graphical user interface, enabling technical and non-technical users to fine-tune models like LLaMA, Mistral, and DeepSeek. Users request access to datasets stored in OpenLedger’s repository, and once approved, these datasets are integrated directly into the ModelFactory workflow. Model selection, configuration, training, and evaluation are all managed through intuitive dashboards and supporting methods like LoRA and QLoRA.
A key feature of ModelFactory is its secure dataset access control, which preserves contributor permissions and ensures responsible data usage. Fine-tuned models can be tested via a built-in chat interface, allowing real-time interactions. The platform also integrates RAG Attribution, which pairs generated outputs with source citations, enhancing transparency and data provenance. With support for modular extensions, live training analytics, and end-to-end model deployment, ModelFactory enables trustworthy, scalable AI model development in a decentralized environment. [7] [8]
Open LoRA
Open LoRA is a scalable framework that efficiently serves thousands of fine-tuned LoRA (Low-Rank Adaptation) models on a single GPU. It optimizes resource use through dynamic adapter loading. It allows just-in-time access to LoRA adapters from sources like Hugging Face or custom filesystems, reducing memory overhead by avoiding preloading all models. Open LoRA supports merging adapters on demand for ensemble inference, enabling flexible and efficient model switching without deploying separate instances.
The framework enhances inference performance with optimizations such as tensor parallelism, flash-attention, paged attention, and quantization, ensuring high throughput and low latency. Its scalability enables cost-effective deployment of many fine-tuned models simultaneously, while features like token streaming and quantization further improve inference speed and efficiency. Open LoRA is especially suited for applications requiring rapid, resource-efficient access to numerous fine-tuned models. [9]
OPN
The OPN token is the foundational utility and governance asset of the OpenLedger ecosystem, designed to power a sustainable, decentralized AI economy. It serves multiple functions, including enabling on-chain governance, paying transaction fees on OpenLedger’s Layer 2 network, and rewarding data contributors, AI developers, and validators. Token holders can vote on ecosystem decisions such as model funding, AI agent policies, and treasury allocations, with delegated governance options available for broader participation.
Beyond governance, OPN is used as gas for L2 transactions, reducing dependence on ETH and allowing for fee models optimized for AI workloads. It also acts as a reward mechanism, with incentives tied to the quality and impact of data contributions and AI service performance. Additionally, the token supports bridging between OpenLedger and Ethereum, enabling cross-chain functionality. A key utility is AI agent staking, where agents must lock up OPN to operate, with underperformance or malicious activity leading to slashing. This staking system enforces quality standards and promotes reliable AI service delivery. Overall, OPN anchors the economic and operational layers of OpenLedger, aligning incentives across AI agents, data providers, and developers. [10]
Partnerships
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