> ## Documentation Index
> Fetch the complete documentation index at: https://benchgen.com/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Overview

> What Train does, when to use it, and what it hands off to Eval and Agents.

# Train Overview

Train is BenchGen's fine-tuning module. It gives you a simple, configuration-driven way to improve a model on your specific task — without managing infrastructure.

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## What Train Does

Upload a dataset, choose a base model, configure a LoRA adapter, and Train handles the rest. When the run completes you download a merged model ready for deployment in Agents or evaluation in Eval.

You get:

* Managed fine-tuning runs (no GPU provisioning)
* LoRA adapter training with configurable hyperparameters
* One-click adapter merging
* Inference endpoint for immediate testing

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## When to Use Train

| Situation                                        | What to do                                           |
| ------------------------------------------------ | ---------------------------------------------------- |
| Eval shows a cluster of failures                 | Export those cases and fine-tune on them             |
| You have domain-specific data to improve quality | Upload a custom dataset and run a targeted fine-tune |
| A merged model is deployed but drifting          | Re-run training with updated data                    |
| You want to test a smaller, cheaper model        | Fine-tune a compact base model on your task          |

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## What Train Hands Off

* **→ Eval**: run a benchmark against the fine-tuned model to measure improvement.
* **→ Agents**: connect the merged model as the LLM inside an agent.

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## Next Steps

* [Add a dataset](/train/add-a-dataset)
* [Fine-tune a model](/train/fine-tune-a-model)
* [Merge & save a model](/train/merge-lora-adapter)
* [Run inference](/train/run-inference)