Reproducible experiments, not a folder of notebooks.
Fine-tune and evaluate open-weights models against your own benchmarks with every run versioned, every hyperparameter recorded and every checkpoint exportable. Built for teams that have to defend a result, not just demo one.
The Problem
Research velocity dies in untracked notebooks and vanished checkpoints. Closed APIs make results unreproducible by construction — you cannot pin a model version you do not control.
How Langtrain Helps
Every run records its dataset hash, seed, hyperparameters, environment and metrics — so a result from six months ago is still defensible.
Define task-specific evals that matter to your work and run them automatically against every checkpoint.
Track how a training set was filtered and deduplicated, so a contamination question has an answer rather than a shrug.
Open weights, standard formats. Export to Safetensors, GGUF or ONNX and reproduce the run anywhere.
Launch hyperparameter sweeps across GPUs and compare runs side by side without writing scheduling code.
Everything in the UI is available from the Python SDK, so experiments live in version control alongside your analysis.
Applications
Vary one component at a time across a sweep and get a comparison table, not twelve notebooks with inconsistent metrics.
Encode the evaluation your field actually cares about and gate checkpoint promotion on it.
Pin a base model by hash, rerun a published recipe on your own data, and record exactly where results diverge.
Auto-generate model cards from tracked run metadata so downstream teams inherit the caveats, not just the weights.