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Simply upload your data, and we will provision GPU infrastructure and run the fine-tune. When it’s done, Oxen.ai will save the fine-tuned model weights directly to your repository, and we spin down the GPU for you. No worrying about run away costs or having to manage your own infrastructure. Once the fine-tuning process is complete, you can deploy your model to a dedicated endpoint and use the inference endpoints to integrate it into your application. Oxen.ai automatically versions and manages the raw model weights and datasets, so that you can always track the data that was used to train the model, or download the model to run locally.

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Why Fine-Tune?

Fine-tuning is a great tool to reach for when basic prompting and context engineering fall short. You may need to fine-tune when:
  • Quality is critical and the model isn’t consistently producing correct outputs.
  • Proprietary Data gives you a unique advantage that generic models can’t capture.
  • Latency is a deal breaker and you need real-time responses.
  • Throughput limitations are bottlenecking your application’s scalability.
  • Ownership of the model is important and you want to control your own destiny.
  • Cost if a foundation model is too expensive for your use case or you want to deploy a smaller model to the edge.
With Oxen.ai, we make it easy to automate the fine-tuning process of LLMs on your own data.

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Modalities

Oxen.ai supports many data types and tasks for fine-tuning.

Text Generation

Fine-tune a model to take a user input as text and generate a single response as text.

Chat Completions

Fine-tune a model on chat messages to have a conversation with a user.

Image Generation

Fine-tune a model to go from text descriptions to images.

Image Editing

Fine-tune a model to take a prompt and a reference image and generate a new image.

Video Generation

Fine-tune a model to take in a prompt and generate a video.

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Start by Uploading a Dataset

To get started, you’ll need to create a new repository on Oxen.ai. Once you’ve created a repository, you can upload your data. The dataset can be in any tabular format including csv, jsonl, or parquet. Once you have your dataset uploaded, you can query, explore, and make sure that the data is high quality before kicking off the fine-tuning process. Your model will only be as good as the data you train it on. When you feel confident that your dataset is ready, use the “Actions” button to select the model you want to fine-tune.

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Selecting a Model

This will take you to a form where you can select the model you want to fine-tune and the columns you want to use for the fine-tuning process. We support fine-tuning for text generation, image generation, image editing, and video generation with a variety of models.
If you want support for any specific models, data formats, training methods contact us at hello@oxen.ai and we are happy to help you get started. We are actively working on support for new models and distributed training.

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Monitoring the Fine-Tune

Once you have started the fine-tuning process, you can monitor its progress. The dashboard will show you loss over time and token accuracy processed. If you are fine-tuning an image or video generation model, you can view the generated images or videos in the “Samples” tab to get a feel for the model’s performance. Click on the “Info” tab to see the fine-tuning configuration and all the hyper-parameters used. This will include a link to the dataset version you used and the raw model weights for downloading and running locally.

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Deploying the Model

Once the model is fine-tuned, you can deploy it to a dedicated endpoint or in the playground. This will give you a /ai/chat/completions api and a playground that you can use to test out the model. Start by using the “playgroud” button. if the model is not loaded you’ll see an “inactive” button on the top right of the playgroud. use the activate button to load the model. To use the API Swap out the model name with the name of the model you want to use.

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Using the Model

Once the model is deployed, you can also chat with it using the Oxen.ai chat interface at the playgroud. Learn more about the chat interface here. For image and video generation, you can use the playground to generate images and videos.

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Downloading the Model

If you want access to the raw model weights, you can download them from the repository using the Oxen.ai Python Library or the CLI. Follow the instructions for installing oxen if you haven’t already.

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Need Custom Infrastructure?

If you need custom or private deployments in your own VPC or want to train a larger model on distributed infrastructure, contact us at hello@oxen.ai and we can give you a custom deployment.