Openledger in two minutes: attribution, datanets, ModelFactory and OpenLoRA
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Short primer on Openledger's AI chain: Proof of Attribution for fair revenue sharing, Datanets of validated datasets, a GUI fine-tuning platform and OpenLoRA for serving many models per GPU.
Key claims
ModelFactory claims up to 3.7x faster training
OpenLoRA claims sub-100ms switching and over 2000 tokens per second
OPEN token covers governance, fees, attribution rewards and staking
It takes you two minutes to get a basic understanding of @openledgerHQ.
Stop doomscrolling and DYOR ↓
- Proof of Attribution: Cryptographically links data contributors transparently and immutably to AI model outputs, ensuring fair compensation.
- Datanets: Decentralized, specialized data networks providing validated domain-specific datasets, improving accuracy and efficiency.
- ModelFactory: GUI-based fine-tuning platform offering advanced methods (LoRA, QLoRA, RAG attribution) with up to 3.7x faster training speeds and superior model performance metrics vs competitors.
- OpenLoRA: Optimized deployment framework supporting thousands of fine-tuned models per GPU, achieving sub-100ms switching, significant memory reduction (8-12GB/GPU), and >2000 tokens/sec throughput.
- All powered by their own native token $OPEN, which is used for governance, paying transaction fees, incentivization through attribution rewards, and crypto-economic security layer (via staking).
Next steps?
Yap.
QUOTING @zerokn0wledge_
. @OpenledgerHQ is a next-gen blockchain designed specifically for AI, that enables the monetization of data, models, and agents through on top of decentralized infra. Let me break down the core concepts ↓ ➤ Proof of Attribution: A cryptographic mechanism ensuring transparent a
. @OpenledgerHQ is a next-gen blockchain designed specifically for AI, that enables the monetization of data, models, and agents through on top of decentralized infra. Let me break down the core concepts ↓ ➤ Proof of Attribution: A cryptographic mechanism ensuring transparent a