Aloukik21 / trainer

AI Trainer - RunPod Serverless

Single-endpoint multi-model LoRA training service using ai-toolkit .

Automatically cleans up GPU memory when switching between different models.

Supported Models

API Usage

List Models

{"input": {"action": "list_models"}}

Check Status

{"input": {"action": "status"}}

Manual Cleanup

{"input": {"action": "cleanup"}}

Train LoRA

{
  "input": {
    "action": "train",
    "model": "flux_dev",
    "params": {
      "dataset_path": "/workspace/dataset",
      "output_path": "/workspace/output",
      "steps": 1000,
      "batch_size": 1,
      "learning_rate": 1e-4,
      "lora_rank": 16
    }
  }
}

Training Parameters

RunPod Deployment

Environment Variables

• HF_TOKEN : HuggingFace token for gated models (required for FLUX, Qwen)

Model Caching

Models are cached at /runpod-volume/huggingface-cache/hub/ for faster subsequent loads.

For optimal cold starts, set the RunPod Model field to one of:

• black-forest-labs/FLUX.1-dev (for FLUX training)
• ai-toolkit/Wan2.2-T2V-A14B-Diffusers-bf16 (for Wan 2.2)
• Qwen/Qwen-Image (for Qwen Image)

Auto-Cleanup

The handler automatically cleans up GPU memory when switching between models:

• Full cleanup when changing model types
• Light cleanup for same model
• Manual cleanup via cleanup action