Supervised fine-tune
Teach on labeled examples
Standard Confidential
Fine-tune any model.
Own your compute.
Pick a base model, bring your data, choose any recipe. Train on shared GPUs or inside an encrypted confidential enclave — then deploy with a single call. No infra, no vendor lock-in.
The pipeline no setup
LLaMA 3.3 8B
Dataset
2.4k pairs
train
Your model
acme-support-v3
ft-job-7c3a
runningLive metrics
- Method
- QLoRA · 4-bit
- Model
- LLaMA 3.3 8B
- Epoch
- 2 / 3
- Loss
- 0.412
Training progresscheckpoint saved
67%
epoch 2/3
~4m 12s remainingstep 2,680 / 4,000
Training loss
0.412eval lossbaseline
Every method
One workspace. Every method.
LoRA, QLoRA, DPO, KTO, SFT, full fine-tune. Pick the recipe, not the vendor. No setup, no waiting on capacity.
Direct preference
Align to what you prefer
Kahneman-Tversky
Pair-free alignment
Low-rank adapt
Fast, memory-light
Quantized LoRA
4-bit efficiency
Full fine-tune
Update every weight
Any modality
Train across every modality.
Text, vision, audio, code, multimodal, video. The same workspace handles them all.
Text-to-text
Instruction copilots, support agents, knowledge search. Train any text model with any method.
LLaMA 3.3Mistral NemoQwen3 Next
Image-to-text
Captioning, screenshot QA, product intelligence. Vision-language reasoning over images.
Qwen-VLLLaVAMiniCPM-V
Audio understanding
Train models to understand and reason about audio content, speech, music, and sounds.
Qwen2-AudioMiniCPM-oQwen2-Audio-7B
Code
Specialized completion, tests, automated refactors. Train models that code, reason, and act.
DeepSeek-CoderStarCoder2CodeLLaMA
Multimodal
Unified copilots that understand documents and UI screenshots. Multimodal reasoning at scale.
Qwen-VLLLaVAKimi-K2
Video
Frame-level understanding, summarization, and reasoning across moving images.
Qwen2-VLLLaVA-NeXTVideo-LLaMA
Built in
Not features. Guarantees.
Confidentiality, compute, and reproducibility. Not checkboxes on a pricing page.
Confidential by default
Your data and weights stay encrypted end to end. Fine-tune without uploading to shared infra.
Any compute, no setup
Single instance or GPU cluster. Traditional or confidential. Switch anytime, with no infrastructure to manage.
Reproducible runs
Every job is versioned with its data, config, and weights. Roll back, branch, or resume any run.
Start fine-tuning in minutes.
No infrastructure. No limits. Bring your data, pick a method, and ship a model that's genuinely yours.