View Models & Deployments
- Models List: Go to Models > Fine-tuned models to see all fine-tuned models with their deployment status. Click a model to open its detail view with Overview, Deployments, and Configurations tabs.
- Deployments List: Each model can have multiple independent deployments. The Deployments tab shows the name, id, status and other attributes of the models.
Create a Fine-Tuned Model
Go to Models > Fine-tuned models > Start fine-tuning and complete the following steps:Step 1: General Details
Enter model name, description, and tags for searchability.Step 2: Select Base Model
Choose your base model source: Platform-hosted: Select from the dropdown list (includes previously imported models). Import from Hugging Face: Select your Hugging Face connection and enter the model name. See Enable Hugging Face for connection setup.Step 3: Fine-Tuning Configuration
Configure training parameters:| Parameter | Description |
|---|---|
| Fine-tuning Type | Full fine-tune, LoRA, or QLoRA (availability depends on model size) |
| Number of Epochs | How many times the model processes the entire dataset |
| Batch Size | Training examples per iteration |
| Learning Rate | Step size during optimization |
| Base Model Parameters | Supported Types |
|---|---|
| < 1B | Full fine-tune, LoRA, QLoRA |
| ≥ 1B and < 5B | LoRA, QLoRA |
| ≥ 5B and ≤ 8B | QLoRA only |
Step 4: Training & Evaluation Datasets
Training Dataset: Select or upload the dataset to train the model. Accepts JSONL, CSV, or JSON files with at least two columns: prompt and completion. Evaluation Dataset (choose one):- Use from training dataset: Allocates a percentage (default 15%) for evaluation
- Upload evaluation dataset: Use a separate dataset
- Skip evaluation: Skip the evaluation step
Step 5: Test Dataset (Optional)
Upload a test dataset to evaluate the fine-tuned model after training completes.Step 6: Hardware Selection
Select the hardware configuration for fine-tuning from the available options.Step 7: Weights & Biases Integration (Optional)
Connect your W&B account to monitor fine-tuning metrics in real-time. Select an existing connection or create a new one. See Integrate with Weights & Biases.Step 8: Review & Start
Review all settings and click Start fine-tuning. The Overview page displays real-time progress.Training Overview
The Overview page displays real-time fine-tuning progress: General Information: Progress status (Initializing, Training in progress, Testing in progress, Fine-tuning completed, Stopped, Failed), total time, and author. Base Model Information: Source model and origin. Training Information: Training type, steps, training loss, validation percentage, validation loss, start time, and duration. Click the arrows next to loss fields to view graphical trends. Test Data Information: Model performance measured by BLEU score. Hardware Information: CPU and GPU utilization during fine-tuning. Training Parameters: Summary of configured parameters. Status handling:- If fine-tuning fails, view the reason and click Re-trigger to restart
- If stopped manually, click Re-trigger to restart from the beginning
Deploy a Fine-Tuned Model
Once fine-tuning completes, deploy the model for inference.Deployment Steps
- Go to the model’s Overview or Model Endpoint page and click Deploy model
- Enter deployment name, description, and tags
- Configure parameters (see below)
- Select hardware for deployment
- Review, accept terms, and click Deploy
Deployment Parameters
Inference Parameters:| Parameter | Description |
|---|---|
| Temperature | Controls randomness in output |
| Maximum Length | Maximum tokens to generate |
| Top P | Nucleus sampling threshold |
| Top K | Number of highest probability tokens to consider |
| Stop Sequences | Tokens that stop generation |
| Inference Batch Size | Concurrent request batching |
| Parameter | Description |
|---|---|
| Min Replicas | Minimum deployed replicas |
| Max Replicas | Maximum replicas for auto-scaling |
| Scale Up Delay | Seconds before scaling up |
| Scale Down Delay | Seconds before scaling down |
Manage Deployed Models
Model Endpoint
After deployment, the API endpoint enables external inferencing. Access via the Model Endpoint tab. The endpoint is available in three formats: cURL, Python, and Node.js. Copy the appropriate format for your integration. Platform usage: Use the deployed model in Prompt Playground or AI Nodes in tool flows.Deployment History
The deployment history tracks all versions of the model:| Field | Description |
|---|---|
| General Details | Name, description, tags, optimization, parameters, hardware, duration |
| Deployment Details | Deployer, timestamps, duration, status (Success/Failed/Deploying) |
| Un-deployment Details | Appears only if undeployed; shows initiator and timestamps |
ModelName_v1, subsequent: ModelName_v2, ModelName_v3, etc. The name persists even if edited.
The most recent deployment is marked with a green tick.
API Keys
Generate API keys for secure external access. Keys are scoped per deployment.- Go to API Keys tab
- Click Create a new API key
- Enter a name and click Generate key
- Copy the key immediately—it won’t be shown again
Configurations
Model Endpoint Timeout: Set timeout duration from 30-180 seconds (default: 60 seconds). Timeout precedence: Tool > Node > Model. Undeploy: Disconnects the model from all active instances immediately. Click Proceed to undeploy. Delete: Removes the model and all associated data. Only available for undeployed models. Click Proceed to delete.Re-deploy a Model
To update deployment parameters or hardware:- Go to the deployed model’s Model Endpoint page
- Click Deploy model
- Modify parameters as needed
- Complete the deployment wizard
Export a Model
Export fine-tuned models for backup or reference:- On the Models page, click the three-dot menu next to the model name
- Select Export model
- The ZIP file downloads to your local machine
Iterative Fine-Tuning
You can fine-tune on top of an existing fine-tuned model:- When selecting a base model, choose a previously fine-tuned model from the Platform-hosted dropdown
- Continue with the standard fine-tuning process