# Fine-tuning the Mochi video generation model on GH200

This guide helps you get started fine-tuning [Genmo's Mochi video generation model](https://www.genmo.ai/) using a [Lambda On-Demand Cloud](https://lambda.ai/instances) GH200 instance.

## Launch your GH200 instance

Begin by launching a GH200 instance:

1. In the Lambda Cloud console, navigate to the [SSH keys page](https://cloud.lambda.ai/ssh-keys), click **Add SSH Key**, and then add or generate a SSH key.
2. Navigate to the [Instances page](https://cloud.lambda.ai/instances) and click **Launch Instance**.
3. Follow the steps in the instance launch wizard.
   - _Instance type:_ Select **1x GH200 (96 GB).**
   - _Region:_ Select an available region.
   - _Filesystem:_ Don't attach a filesystem.
   - _SSH key:_ Use the key you created in step 1.
4. Click **Launch instance**.
5. Review the EULAs. If you agree to them, click **I agree to the above** to start launching your new instance. Instances can take up to five minutes to fully launch.

## Install dependencies

- Install the dependencies needed for this guide by running:

```
    git clone https://github.com/genmoai/mochi.git
    cd mochi-tune
    pip install --upgrade pip setuptools wheel packaging
    pip install -e . --no-build-isolation
    pip install moviepy==1.0.3 pillow==9.5.0 av==13.1.0
    sudo apt -y install bc
    ```

## Download the model weights

- Download the model weights by running:

```
    python3 ./scripts/download_weights.py weights/
    ```

## Prepare your dataset

- Prepare your dataset by following the [README for Genmo's Mochi 1 LoRA Fine-tuner](https://github.com/genmoai/mochi/blob/main/demos/fine_tuner/README.md).

## Begin fine-tuning

- Begin fine-tuning by running:

```
    bash ./demos/fine_tuner/run.bash -c ./demos/fine_tuner/configs/lora.yaml -n 1
    ```

You should see output similar to:

```
    Starting training with 1 GPU(s), mode: single_gpu
    Using config: ./demos/fine_tuner/configs/lora.yaml
    model=weights/dit.safetensors, optimizer=, start_step_num=0
    Found 44 training videos in videos_prepared
    Loaded 44/44 valid file pairs.
    Loading model
    Training type: LoRA
    Attention mode: sdpa
    Loading eval pipeline ...
    Timing load_text_encoder
    Timing load_vae
    Stage                   Time(s)    Percent
    load_text_encoder          0.21     17.34%
    load_vae                   1.01     82.66%

[…]

Sampling: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 64/64 [03:33<00:00,  3.33s/it]
    moving model from cpu -> cuda:0████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 64/64 [03:33<00:00,  1.09it/s]
    moving model from cuda:0 -> cpu
    Moviepy - Building video finetunes/my_mochi_lora/samples/0_200.mp4.
    Moviepy - Writing video finetunes/my_mochi_lora/samples/0_200.mp4
    ```

Note

During the fine-tuning, you'll see messages similar to:

```
    W1126 16:46:47.939000 265211801175072 torch/fx/experimental/symbolic_shapes.py:4449] [2/0_1] xindex is not in var_ranges, defaulting to unknown range.
    W1126 16:46:51.271000 265211801175072 torch/fx/experimental/symbolic_shapes.py:4449] [2/1_1] xindex is not in var_ranges, defaulting to unknown range.
    W1126 16:46:53.847000 265211801175072 torch/fx/experimental/symbolic_shapes.py:4449] [2/2_1] xindex is not in var_ranges, defaulting to unknown range.
    W1126 16:46:56.411000 265211801175072 torch/fx/experimental/symbolic_shapes.py:4449] [2/3_1] xindex is not in var_ranges, defaulting to unknown range.
    ```

These messages can safely be disregarded.

## Cleaning up

When you're done with your instances, terminate them to avoid incurring unnecessary costs:

1. In the Lambda Cloud console, navigate to the [Instances page](https://cloud.lambda.ai/instances).
2. Select the checkboxes of the instances you want to delete.
3. Click **Terminate**. A dialog appears.
4. Follow the instructions and then click **Terminate instances** to terminate your instances.

## Next steps

- To learn how to benchmark your GH200 instance against other instances, see [Running a PyTorch®-based benchmark on an NVIDIA GH200 instance](https://docs.lambda.ai/education/running-benchmark-gh200/).
- For more tips and tutorials, visit the [Education](https://docs.lambda.ai/education/) landing page.