Sovereign LoRA Fine-Tuning
Use the Langtune Sandbox to securely fine-tune LLMs without compromising your proprietary data.
Sovereign Compute
Data Privacy
The Data Privacy Challenge
When utilizing third-party LLM APIs, businesses often face a critical dilemma: to improve the model's performance on domain-specific tasks, they must send their highly sensitive, proprietary data to external servers for fine-tuning. This poses significant security and compliance risks.
Enter Langtune Sandbox
Langtune solves this by providing a Sovereign LoRA fine-tuning sandbox.1. Zero Data Egress: Your training datasets are securely ingested into an ephemeral, isolated VPC.2. Local Weight Updates: The model weights (LoRA adapters) are updated directly within the isolated sandbox.3. Artifact Export: Only the resulting LoRA adapter weights (
.bin or .safetensors files) are exported back to you. The ephemeral compute instance is then securely destroyed.Supported Training Methods
Langtune currently supports:
- •Standard LoRA (Low-Rank Adaptation) for fp16 base models.
- •QLoRA for memory-constrained environments, utilizing 4-bit NormalFloat (NF4) quantization.
- •DoRA (Weight-Decomposed Low-Rank Adaptation) for improved learning capacity and stability.