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Developers can now customize the Mochi 1 video model using LoRA on a single GPU, reducing costs and complexity without sacrificing performance or flexibility.
Mochi, an open-source video model, has recently released a fine-tuning tool that supports Low-Rank Adaptation (LoRA) on a single GPU. This is significant because it makes it easier for developers to customize and adapt the Mochi 1 model without requiring expensive multi-GPU setups. Hereβs what you need to know about setting up and using this tool.
The Mochi team has introduced a new fine-tuner that leverages LoRA, a technique that allows for efficient fine-tuning of large models by only updating a small number of parameters. This is particularly useful for video models like Mochi 1, which can be computationally intensive to train from scratch.
For practitioners, this means you can now fine-tune Mochi 1 on your local machine or a single cloud GPU instance. This is a significant improvement over previous methods that required multi-GPU setups, which are often costly and not feasible for smaller teams or individual developers.
To get started with the Mochi 1 LoRA fine-tuner, follow these steps:

Set Up Inference Code:
git clone https://github.com/genmoai/mochi.git
cd mochi
pip install -r requirements.txt
Download Mochi 1 Weights:
README.md to download the pre-trained weights for Mochi 1.Prepare Your Dataset:
data/
βββ train/
β βββ video_001.mp4
β βββ video_002.mp4
β βββ ...
βββ val/
βββ video_001.mp4
βββ video_002.mp4
βββ ...
Run the Fine-Tuner:
python fine_tuner.py --data_path /path/to/your/data --output_dir /path/to/output --lora_rank 8
--lora_rank parameter controls the rank of the LoRA adaptation, which affects the number of parameters to be updated. A higher rank will generally result in better performance but at the cost of increased computational resources.lora_rank is crucial. It balances between fine-tuning effectiveness and resource efficiency. Start with a lower rank (e.g., 8) and increase it if needed.The Mochi 1 LoRA fine-tuner is a powerful tool for customizing video models on a single GPU. By leveraging LoRA, it reduces the computational overhead and makes fine-tuning accessible to a broader audience. Whether you're working on a personal project or part of a small team, this tool can significantly enhance your ability to adapt Mochi 1 to specific use cases.
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Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside β from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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