together-fine-tuning

To fine-tune an open-source LLM on Together AI end-to-end, you run a workflow script that handles data preparation, uploading training data, training, monitoring, and deploying endpoints. The process uses the Together Python SDK and requires a valid API key.

Yes, you can use DPO and LoRA fine-tuning for Together AI models. The workflow supports LoRA for parameter-efficient adaptation and DPO for preference optimization to align model outputs with specific requirements.

Yes, fine-tuning on Together AI supports vision-language models (VLM). You can fine-tune VLMs for image and text tasks using the provided Python scripts and proper training data formats.

To deploy a private model using BYOM, you need the Together Python SDK, a valid API key, and properly formatted training data. The workflow covers uploading, training, monitoring, and deploying your private model endpoints.

Reasoning fine-tuning injects chain-of-thought capabilities into models, while function-calling fine-tuning improves tool integration. Both are supported by the Together AI workflow to enhance model logic and external tool execution.

Together AI fine-tuning workflows require proper training data formats uploaded via the Together Python SDK. The provided scripts handle data preparation, ensuring your dataset is correctly structured for the specific fine-tuning method chosen.