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Loading a model

To load a model in a custom recipe, you should use the harmony client and provide the model key after the prefix model_registry:// to the asynchronous model method. This method returns a ModelBuilder object, on which you can call several methods to configure how your model will be spawned.
  • .tp() to set the tensor parallelism (TP) of the model (explain in a next section in this page)
  • .with_adapter() to enable lightweight adapter training instead of full parameter fine-tuning (only use this if you are training the model)
  • .into_scoring_model() to convert the model into a scoring model (only use this if you are training the model to predict a scalar value, e.g. when training a value model for PPO or a reward model)
from adaptive_harmony.runtime import recipe_main, RecipeContext

@recipe_main
async def main(ctx: RecipeContext):
    client = ctx.client
    
    # Full parameter Llama 3.1 8B
    policy = client.model(
        path="model_registry://llama-3.1-8b-instruct",
        kv_cache_len=100_000,
        tokens_to_generate=2048,
    ).tp(1)

    # Adapter based Qwen 3 32B, turned into a scoring model (for use as reward model)
    reward_model = client.model(
        path="model_registry://qwen3-32b",
        kv_cache_len=100_000,
        tokens_to_generate=2048,
    ).tp(1).with_adapter().into_scoring_model()

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Spawn methods

ModelBuilder also exposes spawn_train and spawn_inference methods. Adaptive Engine unifies training and inference. Instead of requiring different frameworks/runtimes for training and inference, you can simply spawn models meant for training or inference with spawn_train and spawn_inference. A model spawned with spawn_train will require more GPU memory upfront, since Adaptive makes sure that enough memory is available at spawn time to fit the required max_batch_size during training (model activations, optimizer state, etc…). If you are not training a given model in your recipe (you are spawning a judge model for example), make sure to always spawn it with spawn_inference to reduce GPU memory pressure. The max_batch_size parameter defines the maximum number of tokens that can be allocated in a single training batch - i.e a mini batch that is processed by the model in parallel, the batch size corresponding to each optimization step is user-defined and independent from this parameter. It also limits the maximum sequence length that the model is able to train on. In the worst case scenario, for a dataset of samples with length =~ to max_batch_size, the model will train on a single sample at a time. Any sequences larger than max_batch_size are simply dropped in the training classes, which also reconcile the desired optimization step batch size in # of samples. spawn_train returns a TrainingModel, and spawn_inference returns an InferenceModel; they are both async methods. 
from adaptive_harmony.runtime import recipe_main, RecipeContext

@recipe_main
async def main(ctx: RecipeContext):
    client = ctx.client
    

    train_model = (
        await client.model(
            path="model_registry://llama-3.1-8b-instruct",
            kv_cache_len=100_000,
            tokens_to_generate=2048,
        )
        .tp(1)
        .spawn_train(name="train_model", max_batch_size=4096)
    )

    inference_model = (
        await client.model(
            path="model_registry://llama-3.1-8b-instruct",
            kv_cache_len=100_000,
            tokens_to_generate=2048,
        )
        .tp(1)
        .spawn_inference(name="inf_model")
    )

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Tensor parallelism (tp)

Tensor parallelism (tp) determines how many GPUs a model is split across during execution. Choosing the right tp value depends on your model size and available hardware: larger models typically require higher tp to fit into memory, while smaller models may run efficiently with tp=1. Also, as explained above, a given model that fits on 2 GPUs with tp=2 that was spawned with inference_spawn might not fit if it is spawned with spawn_train. Always ensure that the number you set for tp matches the number of devices you want to use and is supported by your infrastructure.

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Passing model parameters as config input

In custom recipes, you can pass models in the recipe config using the magic class AdaptiveModel. Adaptive will validate that the user-configured parameter for model_to_train below is a valid model key in Adaptive Engine. You can access the path to deploy the model with AdaptiveModel().path 
from typing import Annotated
from adaptive_harmony.runtime import InputConfig, AdaptiveModel

class MyConfig(InputConfig):
    model_to_train: Annotated[AdaptiveModel]
    tp: Annotated[int]
    max_seq_len: Annotated[int]
    train_adapter: Annotated[bool]

@recipe_main
async def main(config: InputConfig, ctx: RecipeContext):
    client = ctx.client

    model_builder = await client.model(config.judge_model.path).tp(config.tp).spawn_inference("judge")
               
    if config.train_adapter:
        model_builder = policy_builder.with_adapter()

    await model_builder.spawn_train("model_trained", config.model_max_seq_len)