> ## Documentation Index > Fetch the complete documentation index at: https://docs.galileo.ai/llms.txt > Use this file to discover all available pages before exploring further. # Config file > Complete reference for the training section. This page explains every field in the `training` section of the YAML config. > The SDK reads a single YAML file that contains both `data_generation` and `training`. > `run_training(config_path=...)` reads the `training` section from that file. ## File format Your YAML file is a **run config** that includes top-level keys and a nested `training` section: ```yaml theme={null} run_steps: ["data_generation", "training"] pipeline_provider: "local" metric_name: "custom" training: metric: {} dataset: {} prompt_template: "" training: {} output: {} model: {} ``` ## Configuration structure The `training` section has these parts: * `metric`: output type and class list (if multi-class) * `dataset`: where to load the dataset created by data generation * `prompt_template`: the exact format the model is trained to follow * `training`: hyperparameters and performance options * `output`: where to write artifacts + optional pushes (Hub/Luna Studio/object store) * `model`: base model + LoRA settings + model download options *** ## `metric` | Field | Type | Required | Default (base config) | Notes | | ---------------- | ---------------------- | ------------- | --------------------- | ----------------------------------------------------------------------------- | | `metric.type` | `string` | Yes | `""` | One of `boolean`, `multi_class`. | | `metric.classes` | `list[string] \| null` | Conditionally | `[]` | Required when `type` is `multi_class`. Order defines the class index mapping. | *** ## `dataset` | Field | Type | Required | Default (base config) | Notes | | -------------------------------- | ---------------- | --------------- | ---------------------------------------- | ------------------------------------------------------------------------------------- | | `dataset.name` | `string` | Yes | `""` | For Hub datasets: `org/repo`. For local: typically the generated dataset folder name. | | `dataset.split` | `string` | Yes | `"train"` | Training split. | | `dataset.test_split_name` | `string` | Yes | `"test"` | Holdout split. | | `dataset.train_label_column` | `string` | Yes | `"label"` | Label column for training. | | `dataset.test_label_column` | `string` | Yes | `"label"` | Label column for evaluation. | | `dataset.local` | `bool` | Yes | `false` | When true, load from disk using `dataset.name` as a path-like identifier. | | `dataset.local_path` | `string` | Yes | `""` | Base path where local datasets exist/extract to. | | `dataset.pull_from_object_store` | `bool` | No | `false` | Download dataset from object store before training. | | `dataset.object_store_bucket` | `string \| null` | If object store | `"${LUNA_OBJECT_STORE_BUCKET:-luna-ft}"` | Required when `pull_from_object_store` is true. | | `dataset.object_store_blob` | `string \| null` | If object store | `"generated_data"` | Required when `pull_from_object_store` is true. | ### Point training at the generated dataset `run_data_generation(...)` returns a dataset path/name. After generation completes, set: * `training.dataset.name` to that value, then run training. *** ## `prompt_template` `prompt_template` is required. * It must reference your dataset columns using `{variable}` placeholders. * For boolean metrics, the model should respond with `"true"` or `"false"`. * For multi-class metrics, the model should respond with a **single class-key token** (`"0"`, `"1"`, ...). Tip: your template variables are validated against dataset columns during preflight. *** ## `training` (hyperparameters) | Field | Type | Required | Default (base config) | | ----------------------------- | -------- | -------- | --------------------- | | `num_train_epochs` | `int` | Yes | `5` | | `per_device_train_batch_size` | `int` | Yes | `1` | | `gradient_accumulation_steps` | `int` | Yes | `8` | | `learning_rate_multiplier` | `float` | Yes | `1.0` | | `max_seq_length` | `int` | Yes | `4096` | | `warmup_steps` | `int` | Yes | `30` | | `weight_decay` | `float` | Yes | `0.01` | | `max_grad_norm` | `float` | Yes | `1.0` | | `logging_steps` | `int` | Yes | `40` | | `torch_compile` | `bool` | Yes | `false` | | `fp16` | `bool` | Yes | `false` | | `bf16` | `bool` | Yes | `true` | | `optim` | `string` | Yes | `"adamw_8bit"` | | `lr_scheduler_type` | `string` | Yes | `"linear"` | | `seed` | `int` | Yes | `3407` | | `use_wandb` | `bool` | Yes | `true` | *** ## `output` | Field | Type | Required | Default (base config) | Notes | | ----------------------------- | ---------------- | --------------- | ---------------------------------------- | ---------------------------------------------------------------------------------- | | `output.model_name` | `string` | Yes | `""` | Artifact folder name and (optionally) Hub model name. | | `output.local_path` | `string` | Yes | `"data/"` | Root directory for artifacts. | | `output.push_to_hub` | `bool` | Yes | `true` | If true, pushes model artifacts to Hugging Face. Requires `HF_TOKEN` when enabled. | | `output.push_to_object_store` | `bool` | No | `false` | Upload artifacts tarball to the configured object store. | | `output.object_store_bucket` | `string \| null` | If object store | `"${LUNA_OBJECT_STORE_BUCKET:-luna-ft}"` | Required when `push_to_object_store` is true. | | `output.object_store_blob` | `string \| null` | If object store | `"finetuned_models"` | Required when `push_to_object_store` is true. | ### Environment variables for pushes * Hugging Face: `HF_TOKEN` (required when `push_to_hub: true`) * W\&B: `WANDB_API_KEY` (optional; warning if missing when `use_wandb: true`) *** ## `model` | Field | Type | Required | Default (base config) | Notes | | ---------------------------------------- | -------------------- | --------------- | ---------------------------------------- | ----------------------------------------------------------- | | `model.name` | `string` | Yes | `"meta-llama/Llama-3.2-3B-Instruct"` | Base model repo id. | | `model.peft_model_map` | `map[string,string]` | No | `{}` | Map base-model → LoRA weights repo id. | | `model.use_unsloth_tokenizer` | `bool` | Yes | `true` | Use Unsloth Tokenizer when available. | | `model.lora_r` | `int` | Yes | `16` | LoRA rank. | | `model.lora_alpha` | `int` | Yes | `16` | LoRA alpha. | | `model.pull_model_from_object_store` | `bool` | No | `false` | Download base model + adapters from object store. | | `model.model_object_store_bucket` | `string \| null` | If object store | `"${LUNA_OBJECT_STORE_BUCKET:-luna-ft}"` | Required when `pull_model_from_object_store` is true. | | `model.model_object_store_blob_base` | `string \| null` | If object store | `"base_models"` | Base model location. | | `model.model_object_store_blob_adapters` | `string \| null` | If object store | `"adapters"` | Adapters location. | | `model.model_local_path` | `string \| null` | No | `""` | Local directory for models (also used for local-only runs). |