
If you are looking for a more hands-on approach to fine-tuning, you can write your own code in Notebooks.
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.
Examples
Here are specific examples of how fine-tuning can be used to solve real-world problems. From coding agents to opitmizing tool calling for your agent, there are a lot of use cases for fine-tuning.- 💬 Chatbot
- 💡 Classification
- 🤖 Agents — Coming soon
- 📄 PDF Extraction — Coming soon
- 📞 24/7 Customer Support Agents — Coming soon
- 🔍 Enterprise Search and Q&A — Coming soon
- 🎨 Brand-Specific Content Creation — Coming soon
- 📝 Automated Report Generation — Coming soon
- 🪛 Quality Control & Anomaly Detection — Coming soon
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 includingcsv
, jsonl
, or parquet
.



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. Right now we support fine-tuning for prompt/response single-turn chat pairs.
If you want support for any larger models or modalities like text-to-image, contact us. We are actively working on support for different data formats and distributed training.
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, token accuracy, the learning rate, and number of tokens processed.
Deploying the Model
Once the model is fine-tuned, you can deploy it to a hosted endpoint. This will give you a/chat/completions
endpoint that you can use to test out the model.

Chatting with the Model
Once the model is deployed, you can also chat with it using the Oxen.ai chat interface. Learn more about the chat interface here.
Downloading the Model Weights
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.