AI Cloud · Post-training
Post-train open-weight models on Nebius and own how they behave.
Use Nebius to fine-tune open-weight models like Llama, Mistral, Qwen, and DeepSeek on your data. Control your model's behaviour, reduce API spend, and keep your AI stack in your own hands.
Why now
Why open-weight and post-training now
Proprietary APIs are effortless to adopt and punishing at scale. Per-token pricing quietly becomes one of your largest cost-of-goods lines - and every product decision is taxed by a price you don't set.
Off-the-shelf models also cap how far you can differentiate. You can't inspect them or guarantee how they behave. Post-training an open-weight model on your data aligns behaviour to your domain and your guardrails, instead of prompt-engineering around a black box.
And ownership is now practical. Open-weight models post-trained on Nebius keep your data, weights, and IP in your hands, with the data residency and governance EMEA enterprises require.
Base models we post-train on Nebius
We work with the leading open-weight families - and pick the one that fits your task, budget, and licence, rather than a single default.
Llama
General-purpose assistants and agents that need strong reasoning and a permissive licence.
Mistral
Fast, cost-efficient models for high-volume inference and tool-using agents.
Qwen
Multilingual document intelligence and retrieval-augmented generation across EMEA languages.
DeepSeek
Heavier reasoning and code workloads where you want frontier-class behaviour you can own.
Gemma
Compact models for on-task assistants and edge-of-budget deployments.
Embedding & reranker models
The retrieval layer behind RAG and search, tuned on your corpus for precision.
How post-training on Nebius works
- 01
Data prep
We curate, clean, and format your data into training and evaluation sets, with PII handling agreed up front.
- 02
Multi-GPU training
Fine-tuning and post-training run on right-sized Nebius GPU clusters, scaled to the model and dataset.
- 03
Evaluation
We benchmark behaviour, quality, and regressions against your tasks before anything ships.
- 04
Distillation
Where it pays off, we distil to a smaller, cheaper model that keeps the behaviour you need.
- 05
Deployment
The model goes to a managed inference endpoint on Nebius, with monitoring and a clean handover.
From models to agents
A post-trained model is the engine behind your agents
Post-training is where an agent gets its judgement. A model tuned on your data and tasks powers meeting, document, workflow, and customer agents that behave the way your business actually works.
Zenvue builds these on Nebius AI Studio and the Token Factory, so the model you own and the agents you run live on the same open infrastructure - no black-box dependency in the middle.
Examples
- Meeting agents that summarise and assign actions
- Document agents for contracts, RFPs, and reports
- Workflow agents that drive multi-step processes
- Customer agents grounded in your knowledge base
No black-box systems
Governance & IP ownership
Owning the outcome means owning the artefacts. Every engagement is structured so that what we build is yours, fully documented, and yours to operate without us.
- You own the model weights produced from your data - they are yours to keep, move, and reuse.
- Code, prompts, evaluation harnesses, and infrastructure-as-code are handed over, documented, and version-controlled.
- Environments are set up in your own Nebius projects with clear roles, access controls, and audit trails.
FAQ
Post-training on Nebius: common questions
What open-weight models does Nebius support for post-training?
You can post-train open-weight families including Llama, Mistral, Qwen, DeepSeek, and Gemma on Nebius. Zenvue helps you pick the base model that fits your task, data, and latency budget.
How long does post-training take on Nebius?
A focused fine-tune on a clear dataset often runs in days, not months. Timelines depend on model size, data volume, and evaluation rounds - Zenvue scopes a realistic plan before any GPU time is spent.
What is the difference between fine-tuning and post-training?
Post-training is the umbrella for adapting a base model after pre-training, including supervised fine-tuning and preference tuning. The goal is the same: make an open-weight model behave consistently on your tasks.
Do I keep the model weights after post-training?
Yes. The weights produced from your data are yours to keep, move, and reuse. That ownership is the whole point of post-training open-weight models instead of wrapping a closed API.
What do you hand over at the end?
You get the post-trained model, the deployment on Nebius, evaluation results, and documentation - so your team can run, retrain, and extend it without depending on us.
Ready to plan your post-training pilot?
Start small, prove the economics, and own the result. We'll scope a focused pilot with you as your partner for Nebius for AI builders.