llama-stack/docs/source/providers/post_training/torchtune.md

---
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---
# TorchTune

[TorchTune](https://github.com/pytorch/torchtune) is an inline post training provider for Llama Stack. It provides a simple and efficient way to fine-tune language models using PyTorch.

## Features

- Simple access through the post_training API
- Fully integrated with Llama Stack
- GPU support and single device capabilities.
- Support for LoRA

## Usage

To use TorchTune in your Llama Stack project, follow these steps:

1. Configure your Llama Stack project to use this provider.
2. Kick off a fine-tuning job using the Llama Stack post_training API.

## Setup

You can access the TorchTune trainer by writing your own yaml pointing to the provider:

```yaml
post_training:
  - provider_id: torchtune
    provider_type: inline::torchtune
    config: {}
```

you can then build and run your own stack with this provider.

## Run Training

You can access the provider and the `supervised_fine_tune` method via the post_training API:

```python
import time
import uuid

from llama_stack_client.types import (
    post_training_supervised_fine_tune_params,
    algorithm_config_param,
)


def create_http_client():
    from llama_stack_client import LlamaStackClient

    return LlamaStackClient(base_url="http://localhost:8321")


client = create_http_client()

# Example Dataset
client.datasets.register(
    purpose="post-training/messages",
    source={
        "type": "uri",
        "uri": "huggingface://datasets/llamastack/simpleqa?split=train",
    },
    dataset_id="simpleqa",
)

training_config = post_training_supervised_fine_tune_params.TrainingConfig(
    data_config=post_training_supervised_fine_tune_params.TrainingConfigDataConfig(
        batch_size=32,
        data_format="instruct",
        dataset_id="simpleqa",
        shuffle=True,
    ),
    gradient_accumulation_steps=1,
    max_steps_per_epoch=0,
    max_validation_steps=1,
    n_epochs=4,
)

algorithm_config = algorithm_config_param.LoraFinetuningConfig(
    alpha=1,
    apply_lora_to_mlp=True,
    apply_lora_to_output=False,
    lora_attn_modules=["q_proj"],
    rank=1,
    type="LoRA",
)

job_uuid = f"test-job{uuid.uuid4()}"

# Example Model
training_model = "meta-llama/Llama-2-7b-hf"

start_time = time.time()
response = client.post_training.supervised_fine_tune(
    job_uuid=job_uuid,
    logger_config={},
    model=training_model,
    hyperparam_search_config={},
    training_config=training_config,
    algorithm_config=algorithm_config,
    checkpoint_dir="output",
)
print("Job: ", job_uuid)

# Wait for the job to complete!
while True:
    status = client.post_training.job.status(job_uuid=job_uuid)
    if not status:
        print("Job not found")
        break

    print(status)
    if status.status == "completed":
        break

    print("Waiting for job to complete...")
    time.sleep(5)

end_time = time.time()
print("Job completed in", end_time - start_time, "seconds!")

print("Artifacts:")
print(client.post_training.job.artifacts(job_uuid=job_uuid))
```