For conceptual background on fine-tuning methods, training requirements, and when to use each approach, see Fine-tuning.
Fine-Tuning Projects
The Fine-Tuning page lists all your projects with the following columns: Project Name, Jobs, Date Created, and Last Modified. From here you can create new projects and open existing ones.
Create a project
Click + Create Project to open the Create Fine-Tuning Project modal. Enter a name (up to 100 characters) and description (up to 1000 characters), then click Create Project. Once created, click Go to Project to open the project and start adding jobs.
Create a fine-tuning job
From inside a project, click + Create Job. The wizard walks through the following steps:
Job Details — Name and describe the job, then select a fine-tuning method:
- Instruction Tuning (Standard) — Fine-tune with domain-specific instructions and responses. Best for reliable outputs that reflect expert knowledge.
- Reinforcement Tuning (Advanced) — Optimize outputs through reinforcement learning. Best for tasks with clear, verifiable answers.
Select Data for Fine-Tuning — Choose an existing file from your storage or upload a new one. Accepted formats: JSONL or Parquet. Maximum file size: 150 MB.
Select Model — Choose a base model to build upon.
Select Hardware Configuration — Choose compute resources on AMD Instinct MI300X hardware:
- 1 Instance — Ideal for fine-tuning a smaller model with a lighter dataset.
- 8 Instances — Recommended for fine-tuning a larger model with an extensive dataset.
Tune Hyperparameters — Adjust training parameters:
- Number of Epochs — Default 1, integer from 1 to 2000.
- Batch Size — Default 1, integer from 1 to 1024.
- Learning Rate — Default 1e-05, number between 0 and 1.
Confirm and Start Job — Review your configuration (job details, data sources, model, hardware, and hyperparameters). Accept the selected model’s license terms, then click Start Job.
Monitor training progress
Each job has a summary page with the following sections:
Job Details — Job name, ID, status, method, creation and completion timestamps, and description.
Loss Metrics — Training loss and validation loss values with a chart tracking both over steps and epochs. Select How to Read This Chart for an explanation of the values.
Accuracy Metrics — Training accuracy and validation accuracy values with a chart tracking both over steps and epochs. Select How to Read This Chart for an explanation of the values.
Data Sources — Training file, training data type, and training prompt used for the job.
Model, Hardware, and Hyperparameters — Base model, hardware configuration, and the full set of parameters used for the run.
Reward Function — Shown for Reinforcement Tuning jobs. Displays the reward criteria (e.g., Math Accuracy, String Check, Text Similarity) with their weights and operations.
Deployment Details — Table of all deployments created from this job, showing Deployment Name, Status, Date Created, and Last Modified.
Deploy a fine-tuned model
After a job completes, deploy the resulting model for inference directly from the job summary page. Existing deployments and their statuses appear in the Deployment Details table at the bottom of the summary.
Demote a deployment
Active deployments can be demoted (undeployed) when no longer needed, freeing infrastructure resources. Last modified on June 22, 2026
