Unsloth
Run and fine-tune supported models with local desktop and training tooling.
Overview
What you provide
- A compatible model, authorized training data and suitable compute
What you get
- Inference responses, fine-tuned weights and training artifacts
Setup & workflow
- Choose the supported desktop or Python installation and follow a model-specific training guide.
- Prepare the input: A compatible model, authorized training data and suitable compute.
- Run a small, reversible example and inspect the output: Inference responses, fine-tuned weights and training artifacts.
- Inspect training data rights and validate the fine-tuned model on held-out examples.
Requirements & installation
- Configure the documented runtime and authorized service access before attempting this workflow.
Limits & review
- Core Apache-2.0 and optional AGPL components have different obligations; training needs compute.
- Evidence review only: the product was not installed or tested in this crawl.
Your part
- Inspect training data rights and validate the fine-tuned model on held-out examples.
When to consider another product
Unreviewed production decisions, unrestricted account access or guaranteed factual results.
Plans & billing details
Cost planning
- Review scope is licensing and documented free-use boundaries, not numeric prices or current hosted-plan allowances.
Published prices are a snapshot. Confirm billing cycle, taxes and current allowances with the provider.
Sources & verification3
This profile is based on official sources, not a hands-on product test.
Vendor descriptions and demos document advertised features. Editorial guidance is based on these sources.
Discovered via OpenFree.Tools.
- Unsloth — official READMEraw.githubusercontent.com
The maintainer README supports this scope: Run and fine-tune supported models with local desktop and training tooling.
- Unsloth — product entry pointunsloth.ai
The public product entry point was retrieved. Functional scope and setup in this record are grounded in the linked maintainer README.
- Unsloth — license termsraw.githubusercontent.com
The retrieved license materials support this boundary: Apache-2.0 core; optional AGPL-3.0 components. Review the complete terms for your use case.
Frequently asked questions
What does Unsloth do?
Run and fine-tune supported models with local desktop and training tooling.
How much does Unsloth cost?
Software or published weights are available under Apache-2.0 core; optional AGPL-3.0 components. Model inference, external services and your own compute are separate; hosted plan prices were not reviewed.
What should I check before using Unsloth?
Core Apache-2.0 and optional AGPL components have different obligations; training needs compute.