Fine-Tuned Models: Private Customization

Fine-tune foundation models on proprietary data inside TEEs. Better accuracy, zero data leakage. Keep your training data, gradients, and custom weights encrypted with hardware-enforced privacy.

Start Fine-Tuning Contact Us

LoRA & PEFT

Multi-GPU training

Sealed checkpoints

Training attestations

Fast deployment

Gradient privacy

Why It Matters

Why Private Fine-Tuning Matters

Custom performance demands private corp data; Phala lets you use it safely.

Training data contains business secrets

Traditional cloud infrastructure exposes sensitive information to operators and administrators.

More Information

Fine-tuned weights encode proprietary knowledge

Hardware-enforced isolation prevents unauthorized access while maintaining computational efficiency.

More Information

Model gradients can leak training examples

End-to-end encryption protects data in transit, at rest, and critically during computation.

More Information

Vendors should never see your data or weights

Cryptographic verification ensures code integrity and proves execution in genuine TEE hardware.

More Information

How It Works

End-to-end confidential fine-tuning with hardware attestation and encrypted artifacts.

TEE Verified & Unsealed

QLoRA 2× Faster

Auto Safety Checks

Encrypted Export

Base Model Loaded

Dataset Streamed Privately

Optional DPO/GRPO

OpenAI Endpoint Ready

🔐

Remote Attestation

TEE Verified & Keys Unsealed

📦

Load Base Model

Llama / Mistral / Qwen

🗂️

Encrypted Dataset

streamed privately

🔥Fine-Tuning Loop Unsloth

Apply QLoRA

Train with Unsloth

2× Faster · 70% Less VRAM

Optional DPO/GRPO

Safety Checks

PII, Toxicity, Bias

🧾

Export Encrypted LoRA

+ Attestation Report

🚀

Deploy on Phala TEE

OpenAI / HF endpoint

Fine-Tuning LLaMA 3 with Unsloth on Phala Cloud

7-Step Tutorial: Confidential fine-tuning with hardware attestation and encrypted artifacts

Environment Setup

Install Unsloth and Hugging Face libraries with GPU support

Loading Chat Dataset Securely

Mount and load encrypted fine-tuning dataset in conversational format

Loading LLaMA 3 with Unsloth

Load base model with 4-bit quantization and memory optimization

Applying LoRA Adapters

Add Low-Rank Adapters to attention and feed-forward layers

Fine-Tuning with TRL

Supervised fine-tuning using HuggingFace TRL SFTTrainer

Merging LoRA into FP16 Weights

Merge LoRA adapters into base model for deployment

Saving and Uploading Model

Push merged model to Hugging Face Hub for inference

# Install Unsloth and Hugging Face libraries
pip install unsloth transformers accelerate trl datasets

# (Optional) Ensure PyTorch 2.1 with CUDA 12.1 is installed for H200 GPU
pip install torch==2.1.0

# Verify that the GPU is accessible
python -c "import torch; print(torch.cuda.is_available(), torch.cuda.get_device_properties(0).name)"