Fine-tune on your data, deploy through the governed gateway.
LoRA fine-tuning jobs from your knowledge bases and datasets, artifacts written to your own object storage, registered to the model library, and deployed to dedicated inference — with evals to verify quality gains before any model reaches production.
The complete fine-tuning loop inside your infrastructure.
LoRA Fine-Tuning Jobs
Submit fine-tuning jobs using LoRA adapters on top of base open-weight models. Efficient training that reduces GPU hours while preserving base model quality. Supports Llama, Mistral, Qwen, Gemma, and other popular families.
Train from Knowledge Bases & Datasets
Use documents already indexed in Workspace knowledge bases as training data, or upload custom datasets in JSONL format. Data preprocessing, chunking, and deduplication handled automatically.
Artifacts to S3 or MinIO
Trained adapter weights are written to your configured object storage — AWS S3, Google Cloud Storage, or self-hosted MinIO. You own the artifacts, stored in your environment, exportable at any time.
Register to Model Library & Deploy
After training completes, register the fine-tuned adapter to the ManyLayers model library with one click. Deploy to dedicated inference immediately — from training run to governed endpoint without manual steps.
Evals to Verify Gains
Run automated evaluation suites against your fine-tuned model before promoting it to production. A/B comparisons against the base model on your benchmark tasks are scored and ranked on the ELO leaderboard.
Data Privacy During Training
Training jobs run on your own infrastructure. Training data, intermediate weights, and final adapters never leave your environment. No training data used to improve any shared model.
From dataset to production-ready model.
Prepare your dataset
Upload a JSONL training file or point the job at an existing Workspace knowledge base. ManyLayers validates format, deduplicates, and splits into train and validation sets automatically.
Configure and submit the job
Select the base model, LoRA rank, learning rate, and epoch count. Submit via the UI or API. Training runs on your GPU nodes and emits live loss and metric streams.
Evaluate, register, and deploy
Run evals against your benchmark suite to verify quality gains. Register the passing adapter to the model library. Deploy to dedicated inference — or keep testing with more training iterations.
Training and deployment as one governed workflow.
Most fine-tuning workflows are disconnected from serving infrastructure — artifacts land in object storage and someone has to wire them into a serving stack manually. ManyLayers closes this loop: training artifacts register directly to the model library and deploy to the governed gateway without leaving the platform.
- LoRA rank, alpha, and target modules configurable per job
- Multi-GPU training supported via FSDP or DeepSpeed Zero on Kubernetes
- Training artifacts versioned — roll back to a previous adapter at any time
- Eval results recorded in the ELO leaderboard alongside other model comparisons
- Fine-tuning endpoint compatible with OpenAI fine-tuning job API for tooling compatibility
“We fine-tuned Mistral on our support ticket history, ran the evals, and had a domain-adapted model serving in production the same week — all without data leaving our environment. That would have taken a quarter to build ourselves.”
VP of Engineering — Enterprise Customer Experience Platform