Fine-Tune AI Models on Your Hardware: Complete Privacy

Why Fine-Tune Locally?

Fine-tuning adapts a pre-trained model to your specific data—your writing style, your domain terminology, your company's codebase. Cloud fine-tuning services require you to upload your training data to their servers, which may include proprietary, sensitive, or personal information.

Local fine-tuning keeps your data on your hardware. The trained model is yours alone.

What is LoRA?

LoRA (Low-Rank Adaptation) is the most practical fine-tuning technique for consumer hardware. Instead of updating all model weights (which requires enormous VRAM), LoRA trains a small set of adapter weights that modify the model's behavior.

Key advantages:

• Low VRAM: Fine-tune a 7B model with just 8 GB VRAM
• Fast: Training completes in minutes to hours, not days
• Composable: Swap LoRA adapters without reloading the base model
• Small files: A LoRA adapter is typically 10–100 MB vs the full model's 4–14 GB

Fine-Tuning Language Models

To fine-tune a language model on your data:

1. Prepare a JSONL dataset with prompt/completion pairs
2. Choose a base model (Llama 3.1 8B is ideal for first attempts)
3. Configure training: learning rate (2e-4), epochs (3), rank (16)
4. Launch training through the Thumper agent or command line
5. Test the adapter by loading it into Ollama

The model management system tracks your custom adapters alongside base models.

Fine-Tuning Image Models

For Stable Diffusion LoRAs:

1. Collect 15–30 images of your subject (consistent style and quality)
2. Caption each image (use an AI captioner or write manually)
3. Train with Kohya_ss or SimpleTuner (both in the Thumper catalog)
4. Load the LoRA into ComfyUI

SDXL LoRA training needs 10–12 GB VRAM. SD 1.5 LoRAs train on 6 GB.

VRAM Requirements

• 7B LLM LoRA: 8 GB VRAM minimum
• 13B LLM LoRA: 12–16 GB VRAM
• SD 1.5 LoRA: 6 GB VRAM
• SDXL LoRA: 10–12 GB VRAM
• Full fine-tune (7B): 24 GB+ VRAM (use LoRA instead if possible)

Check the VRAM guide for more details.

Privacy Considerations

When you fine-tune on cloud services, your training data is typically:

• Stored on their servers for 30+ days
• Potentially used to improve their service
• Subject to their data retention policies

When you fine-tune locally:

• Training data never leaves your disk
• The trained adapter uses your configured local disk and access controls
• Sharing or syncing an adapter is a separate opt-in workflow with its own documented security boundary

Use Cases

• Custom writing assistant trained on your published articles
• Domain-specific chatbot for legal, medical, or technical knowledge
• Brand-consistent image generation with LoRA-trained style
• Code completion tuned to your project's patterns and conventions

Ready to try it? Download Thumper-Run free →