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

# TRAINING

> Training recipes

## OVERVIEW

A training recipe captures a complete model training configuration (base model, datasets, training method, and hyperparameters) as a reusable JSON template. Running a recipe produces a consistent, reproducible training job without requiring you to reconfigure inputs each time.

Training recipes are saved from the Oumi Builder and accessed from the **Recipes** page. For details on saving and running a recipe from the UI, see [Training Recipes](/guides/training/recipes).

For the full schema reference, see [Model Training Recipe Schema](/reference/schema/models).

***

## COMMON RECIPE PATTERNS

### SUPERVISED FINE-TUNING WITH LORA (PEFT)

The most common starting point. LoRA reduces GPU memory requirements and trains faster than full fine-tuning, making it ideal for rapid iteration.

```json theme={null}
{
  "model": {
    "modelName": "meta-llama/Llama-3.1-8B-Instruct",
    "torchDtype": "bfloat16",
    "modelMaxLength": 4096
  },
  "data": {
    "train": {
      "datasets": [{ "datasetId": 101 }]
    },
    "validation": {
      "datasets": [{ "datasetId": 102 }]
    }
  },
  "training": {
    "trainerType": "sft",
    "numTrainEpochs": 3,
    "learningRate": 0.0002,
    "lrSchedulerType": "cosine",
    "warmupRatio": 0.05,
    "maxGradNorm": 1.0,
    "mixedPrecisionDtype": "bf16",
    "enableGradientCheckpointing": true,
    "evalStrategy": "epoch",
    "saveFinalModel": true,
    "runName": "llama3-8b-lora-v1"
  },
  "peft": {
    "usePeft": true,
    "peftMethod": "lora"
  }
}
```

**When to use:** Most fine-tuning tasks, especially when iterating quickly or working with limited GPU memory.

***

### FULL-WEIGHT FINE-TUNING (FFT)

Updates all model parameters. Use when you need deep behavioral changes or have sufficient compute available.

```json theme={null}
{
  "model": {
    "modelName": "Qwen/Qwen3-8B",
    "torchDtype": "bfloat16",
    "modelMaxLength": 8192,
    "attnImplementation": "flash_attention_2"
  },
  "data": {
    "train": {
      "datasets": [{ "datasetId": 201 }]
    },
    "validation": {
      "datasets": [{ "datasetId": 202 }]
    },
    "test": {
      "datasets": [{ "datasetId": 203 }]
    }
  },
  "training": {
    "trainerType": "sft",
    "numTrainEpochs": 2,
    "learningRate": 0.00005,
    "lrSchedulerType": "linear",
    "warmupSteps": 100,
    "weightDecay": 0.01,
    "mixedPrecisionDtype": "bf16",
    "enableGradientCheckpointing": true,
    "evalStrategy": "steps",
    "evalSteps": 500,
    "saveSteps": 500,
    "saveFinalModel": true,
    "enableWandb": true,
    "runName": "qwen3-8b-fft-v1"
  },
  "peft": {
    "usePeft": false,
    "peftMethod": "lora"
  }
}
```

**When to use:** When LoRA quality is insufficient, or you're making substantial domain adaptation changes.

***

### ON-POLICY DISTILLATION

Trains a smaller student model guided by a stronger teacher. Requires `trainerType: "opd"`. See [On-Policy Distillation](/guides/training/on-policy-distillation) for configuration details.

```json theme={null}
{
  "model": {
    "modelName": "HuggingFaceTB/SmolLM2-1.7B-Instruct",
    "torchDtype": "bfloat16",
    "modelMaxLength": 2048
  },
  "data": {
    "train": {
      "datasets": [{ "datasetId": 301 }]
    }
  },
  "training": {
    "trainerType": "opd",
    "numTrainEpochs": 3,
    "learningRate": 0.0001,
    "lrSchedulerType": "cosine",
    "warmupRatio": 0.1,
    "mixedPrecisionDtype": "bf16",
    "saveFinalModel": true,
    "runName": "smollm-distilled-v1"
  },
  "peft": {
    "usePeft": true,
    "peftMethod": "lora"
  }
}
```

**When to use:** When you want a smaller, faster model that approximates a larger teacher's behavior on a specific task.

***

## TIPS

* **Start with LoRA:** switch to FFT only if LoRA quality is insufficient for your task.
* **Use `evalStrategy: "epoch"`** for small datasets; use `"steps"` with a reasonable `evalSteps` for large ones.
* **Set `runName`:** descriptive names make it easier to compare runs in the activity log.
* **Use `inferenceSeed`** for reproducible results across recipe runs.
* **Enable `enableGradientCheckpointing`** when GPU memory is constrained; it slows training slightly but allows larger batch sizes.