I built an LLM debugger for fine-tuning failures

Description and use

**Project Summary:**

This open-source project provides an LLM (Large Language Model) debugger for identifying and addressing fine-tuning failures, enabling developers to add AI capabilities without starting from scratch. The debugger is suitable for prototype development, building RAG (Retrieval-Augmented Generation) workflows, and evaluating model tooling. However, it requires manual inspection and verification of dependencies, maintenance, and documentation before adoption.

**Value:**

The primary value proposition of this project lies in its ability to streamline the fine-tuning process for LLMs, saving developers time and effort. By providing a debugger, it enables them to quickly identify and address issues, thus accelerating the development of AI-powered applications.

**Practical Adoption Path:**

To adopt this project, developers should follow these steps:

1. **Manual Inspection:** Carefully review the project's documentation, code, and dependencies to understand its functionality and potential limitations.
2. **Dependency and Maintenance Checks:** Verify that the project's dependencies are up-to-date and that the maintenance schedule is reasonable.
3. **Verify License and Release Cadence:** Ensure that the project's license is suitable for your use case and that the release cadence is regular and reliable.

Before adopting

Review the current source documentation, licensing terms, dependencies, and maintenance status against your intended use. [View the source](https://github.com/gradian-ai/gradian).

Use in a workflow

I built an LLM debugger for fine-tuning failures helps add AI capability without starting from a blank model stack.

• prototype AI features
• build RAG or agent workflows
• evaluate model tooling

Limitations and review notes

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.