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ModelFactory is a user-friendly fine-tuning platform for Large Language Models (LLMs) within the OpenLedger ecosystem. Unlike conventional fine-tuning solutions that rely on APIs or command-line setups, ModelFactory offers a fully GUI-based experience, making model customization accessible to everyone. It enables users to fine-tune AI models using permissioned and verified datasets approved through @OpenLedger
Core Concepts: How ModelFactory Works
ModelFactory combines secure dataset management with AI model fine-tuning in a streamlined and user-friendly workflow, ensuring both data protection and ownership transparency. The platform operates through several key components:
Model Selection & Setup
Users can choose from multiple leading LLMs such as LLaMA, Mistral, and DeepSeek. Training parameters including learning rate, batch size, and training epochs can be adjusted directly through the graphical interface without requiring coding knowledge.
Fine-Tuning Engine
ModelFactory supports efficient fine-tuning techniques like LoRA and QLoRA, enabling faster and more resource-efficient model customization. Integrated real-time dashboards allow users to monitor training performance, progress, and optimization metrics throughout the process.
Interactive Chat Interface
The platform includes a built-in chat environment where users can test and interact with their fine-tuned models instantly through the GUI or via API access. This supports live Q&A experiences, workflow automation, and specialized task interactions in real time. #OpenLedger #SECHaltsInnovationExemption #ECBOpposesEuroStablecoinExpansion #USDCCirculationUp400MWeekly
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Większość ludzi nadal postrzega Solanę jako „szybką sieć.”
Ta narracja jest przestarzała.
To, co się dzieje na $SOL teraz, jest znacznie większe niż prędkość.
Solana ewoluuje w jeden z najsilniejszych ekosystemów kryptowalutowych dla konsumentów na rynku — gdzie handel, płatności, DePIN, aplikacje AI, gry i produkty konsumpcyjne ze świata realnego konwergują na jednej sieci.
Dlaczego to ma znaczenie?
Ponieważ następna hossa może nie być wygrana przez sieć z najlepszą technologią. Zwycięży ekosystem, który przyciągnie najwięcej użytkowników, twórców, płynności i uwagi.

Czym są Datanets?
@OpenLedger wprowadza Datanets — zdecentralizowane sieci danych zaprojektowane do zasilania nowej generacji specjalizowanych modeli AI. Te sieci zbierają, weryfikują i dystrybuują wysokiej jakości zestawy danych specyficznych dla danej dziedziny, wymagane do szkolenia i dostosowywania AI.
Datanets działają jako przejrzyste i uporządkowane repozytoria danych, w których uczestnicy mogą przesyłać cenne zestawy danych z weryfikowalną atrybucją. Każdy wkład jest śledzony przez zdecentralizowany mechanizm, zapewniając wiarygodność danych, sprawiedliwość i zaufanie bez polegania na centralnej kontroli.

@OpenLedger Proof of Attribution framework creates a transparent and decentralized system that permanently connects AI model outputs to the data and contributors behind them. By using cryptographic verification and on-chain attribution, every contribution can be traced, validated, and rewarded fairly.
Step 1: Data Contribution
Contributors submit structured, high-quality datasets designed for specific AI training purposes. Each dataset is recorded on-chain, creating a verifiable and transparent history of ownership and usage.
Step 2: Datanets and Contribution Analysis
Training data is submitted along with metadata that defines how it should be used within the ecosystem. OpenLedger then evaluates the importance of each contribution through:
Feature-Level Influence: Measuring how much the data improves or shapes model performance.Contributor Reputation: Assessing the reliability, consistency, and past quality of the contributor’s submissions.
Step 3: Training and Verification
During model training, influence scores are calculated to determine the relevance and effectiveness of each dataset. Comprehensive training logs verify that all contributions are properly tracked and authenticated throughout the process.
Step 4: Attribution-Based Reward Distribution
Contributors are rewarded with tokens based on the measurable impact their data has on AI model outputs. The system is designed to prioritize meaningful and high-value contributions, ensuring fair compensation across the network.
Step 5: Filtering Malicious or Low-Quality Data
Datasets identified as biased, duplicated, adversarial, or low quality are penalized through mechanisms such as stake slashing. Contributors who repeatedly submit harmful or poor-quality data receive reduced future rewards, helping maintain the integrity and reliability of the AI ecosystem.
This end-to-end attribution pipeline enables a trustless and provable reward structure where valuable contributors are incentivized, transparency is maintained, and AI model quality remains protected.
$OPEN #OpenLedger