{"library":"prodigy-plus-schedule-free","title":"ProdigyPlusScheduleFree","description":"Automatic learning rate optimizer combining Prodigy's adaptive LR with Schedule-Free's constant-parameter interpolation. Version 2.0.1 improved weight decay handling. Active development.","language":"python","status":"active","last_verified":"Fri May 01","install":{"commands":["pip install prodigy-plus-schedule-free"],"cli":null},"imports":["from prodigy_plus_schedule_free import ProdigyPlusScheduleFree"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import torch\nfrom prodigy_plus_schedule_free import ProdigyPlusScheduleFree\n\nmodel = torch.nn.Linear(10, 2)\noptimizer = ProdigyPlusScheduleFree(model.parameters(), lr=1.0)\noptimizer.train()\nfor data, target in [(torch.randn(10), torch.tensor(1))]:\n    optimizer.zero_grad()\n    loss = torch.nn.functional.cross_entropy(model(data), target.unsqueeze(0))\n    loss.backward()\n    optimizer.step()","lang":"python","description":"Basic usage: instantiate optimizer, call .train() before training loop, step normally.","tag":null,"tag_description":null,"last_tested":null,"results":[]},"compatibility":null}