openledger
  • Openledger Ecosystem
    • What is Openledger?
    • Test Network Overview
      • Block Production and Execution
        • Data Availability via EigenDA
        • Bridging and Settlement
    • Data Intelligence Layer
  • Testnet
    • Node Installation
      • Android
      • Windows
      • Chrome Extension
      • Linux (Ubuntu)
    • Earning Mechanism
      • Network Earnings
      • Referral Earnings
      • Tier System and Progression
  • Epoch 2
  • DATANETS AND PROOF OF ATTRIBUTION
    • What is Datanets?
    • Why Specialized Data is important?
    • Proof of Attribution
    • OpenLedger Data Attribution Pipeline
    • RAG Attribution
  • Token
    • Openledger Token
  • Model Factory
    • ModelFactory: Where AI Meets Secure Data Fine-Tuning
    • Core Concepts
    • Supported Models
    • System Architecture
    • Key Features
    • Benchmarks
  • OpenLora
    • Open LoRA: A Scalable Fine-Tuned Model Serving Framework
    • System Architecture
    • Workflow
    • Optimizations & Performance Enhancements
    • Use Cases
    • API & Integration
    • The Future
  • Community Support
    • Openledger communities
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  1. OpenLora

API & Integration

1. Load Adapter on Demand

POST /load_adapter
Content-Type: application/json

{
  "adapter_name": "my_finetuned_model",
  "source": "huggingface"
}
  • Loads a specific LoRA adapter from Hugging Face dynamically.

2. Merge Multiple Adapters

POST /merge_adapters
Content-Type: application/json

{
  "adapters": ["adapter_1", "adapter_2"],
  "merge_type": "ensemble"
}

Merges multiple adapters for improved model performance.

3. Run Inference

POST /generate
Content-Type: application/json

{
  "prompt": "Explain quantum computing in simple terms.",
  "adapter": "scientific_finetuned"
}

Runs inference with a selected fine-tuned adapter.

4. Unload Adapter

POST /unload_adapter
Content-Type: application/json

{
  "adapter_name": "scientific_finetuned"
}
  • Unloads the adapter to free GPU memory.

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Last updated 3 months ago