Fine-tuning
Fine-tuning from dataset to model
Adapt a model to your examples, review the setup, follow the run and download its outputs. Start in New run > Supervised fine-tuning (SFT). Managed recipes are offered when supported by the deployment. Custom code lets you bring your own training program.
Models and methods
- Choose a LoRA adapter or full model weights, with BF16, FP16 or FP32 precision. Compatible LoRA configurations also offer 4-bit QLoRA.
- Start with pinned Qwen3 0.6B, 1.7B or 4B presets, or supply a Hugging Face model and exact commit. Managed models must work with Transformers without remote code.
- Set epochs or maximum steps, learning rate, batch size, gradient accumulation, sequence length, LoRA rank and alpha, evaluation fraction and random seed. Truncating long examples is an explicit choice.
Data and launch review
Upload JSONL, JSON or CSV, including archives, or use a saved dataset, Hugging Face repository or direct HTTPS download. Map your own column names and select files. SFT accepts input/output pairs or conversations ending in an assistant answer. Private or gated dataset tokens are used for import only.
Validate configuration checks the selected examples before starting compute. The review includes dataset counts, a preview, the model, resources and spending limit. You can load or export configuration JSON and export the reviewed workload. Editing the setup requires another review. Managed inspection accepts up to 256 files, 256 MB and 200,000 examples, with at least five distinct prompts.
Progress, recovery and results
Follow logs and reported training measurements. The managed trainer saves its model or adapter, optimizer and progress state for supported recovery. Successful runs export model or adapter files, a loading script, the recipe, dataset split identities, held-out measurements and checksums. An adapter still needs its pinned base model. Evaluation reports the held-out result without promising a quality improvement.
Managed text recipes use one CUDA GPU. Use your own code for other frameworks, larger datasets or distributed training.