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Model Fine-Tuning

Manage end-to-end AI model fine-tuning on 0G's distributed GPU network.

Updated Jun 18, 2026
Search Tags:#model training#fine-tuning#task monitoring#dataset upload#0g compute#model decryption
One-click install
npx skills add https://github.com/srijan399/imsy --skill model-fine-tuning-srijan399
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Model Fine-Tuning
Source: https://github.com/srijan399/imsy/tree/main/.0g-skills/skills/compute/fine-tuning
Command: npx skills add https://github.com/srijan399/imsy --skill model-fine-tuning-srijan399

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill streamlines the full lifecycle of fine-tuning an AI model on 0G’s distributed GPU network, reducing the manual work needed to prepare data, fund jobs, monitor progress, and retrieve the final model.

Core Features & Use Cases

  • Provider and model discovery: Lists available fine-tuning providers and supported base models before starting a job.
  • Dataset and task management: Uploads training data, calculates token or cost estimates, creates fine-tuning tasks, and tracks status through completion.
  • Model retrieval workflow: Handles download, acknowledgment, and decryption of the trained model once delivery is complete.
  • Use case: A developer preparing a custom classifier can upload a dataset, fund the provider, launch training, and later decrypt the finished model for deployment.

Quick Start

Ask the Skill to guide you through fine-tuning a model on 0G by preparing your dataset, selecting a provider, creating the task, and monitoring it until the model is ready to decrypt.

Frequently Asked Questions about Model Fine-Tuning

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune a custom AI model on 0G compute?▼

To fine-tune a model on 0G compute, you discover a provider, upload your dataset, create the task, fund the transfer, and monitor progress until completion. The workflow finishes with downloading and decrypting the trained model.

What steps are required to upload a dataset and start model training on a distributed GPU network?▼

Starting model training requires uploading your dataset, calculating token and cost estimates, validating the data, and creating a fine-tuning task. You then transfer funds to the chosen provider to initiate the training job.

Can I track fine-tuning task status and monitor progress on 0G?▼

Yes, you can track fine-tuning task status and monitor progress on 0G. The process includes checking task states, monitoring the training job through completion, and performing an acknowledgement step before retrieving your model.

How do I retrieve and decrypt my trained model after fine-tuning is complete?▼

To retrieve your trained model after fine-tuning, you download the delivered output, complete the required acknowledgement step, and then decrypt the model file for deployment.

Does fine-tuning on 0G compute support provider and base model discovery?▼

Yes, fine-tuning on 0G compute supports provider and base model discovery. Before starting a job, you can list available fine-tuning providers and view supported base models to select the right environment.

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