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Gradian

Gradian is a training-data attribution engine for LoRA fine-tuned LLMs. Given a fine-tune that regressed a capability, it ranks the training examples responsible using curvature-corrected influence functions, groups them into clusters, and cross-checks the verdict against 41 deterministic checks over the dataset, config and trainer logs.

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# Gradian

> Gradian is a training-data attribution engine for LoRA fine-tuned LLMs. Given a fine-tune that regressed a capability, it ranks the training examples responsible using curvature-corrected influence functions, groups them into clusters, and cross-checks the verdict against 41 deterministic checks over the dataset, config and trainer logs.

## Key facts

- Gradian analyzes a fine-tune that already exists. It does not train your model. You fine-tune first, then Gradian explains.
- Attribution requires a PEFT LoRA adapter on the linear layers of a HuggingFace causal LM. Full fine-tunes cannot be attributed over.
- `gradian diagnose` needs no GPU and no adapter, and is the right first command.
- `gradian train` exists but is an optional convenience, needed only for counterfactual retraining.
- Version 0.1.0, Apache-2.0, alpha, not yet on PyPI.

## Documentation

- [What Gradian is](https://gradian.dev/docs.md): A training-data attribution engine for LoRA fine-tuned LLMs. It tells you which training examples, or which config setting, caused a capability to regress.
- [Install](https://gradian.dev/docs/install.md): Install Gradian with the script or from source, understand the supported dependency window, and verify the environment with gradian doctor.
- [Quickstart](https://gradian.dev/docs/quickstart.md): Audit a run in seconds with no GPU, then attribute a regressed capability to the training examples that caused it.
- [The workflow](https://gradian.dev/docs/workflow.md): Where Gradian sits relative to your training. You fine-tune first, Gradian explains afterwards, and gradian train is an optional convenience.
- [How it works](https://gradian.dev/docs/how-it-works.md): The three lanes, per-example gradients in one backward pass, the influence math, and the design decisions that came out of measurements.
- [A worked example](https://gradian.dev/docs/example.md): One recorded run end to end. The actual training rows, the actual eval file, the command, and how those rows became the verdict.
- [CLI reference](https://gradian.dev/docs/cli.md): Every gradian command and every flag, with what it does and when you need it.
- [Configuration](https://gradian.dev/docs/configuration.md): Every field in gradian.yaml, with its type, default and effect. Anything that changes a number lives here and is hashed into the index id.
- [Diagnostics](https://gradian.dev/docs/diagnostics.md): The 41 deterministic checks Gradian runs over your dataset, config and trainer logs, with no GPU and no attribution involved.
- [Limitations](https://gradian.dev/docs/limitations.md): What Gradian will not do, and the constraints that decide whether it fits your setup at all.
- [Troubleshooting](https://gradian.dev/docs/troubleshooting.md): The errors you are most likely to hit, what actually causes them, and the fix.
- [GPU validation](https://gradian.dev/docs/gpu-testing.md): How to run the real validation suite on a GPU, what each experiment establishes, and what went
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