{"record":{"id":"1b8e337670089a7f","repo":"huggingface/pytorch-image-models","slug":"invalid-learning-rate-lr-1b8e33","errorCode":null,"errorMessage":"Invalid learning rate: {lr}","messagePattern":"Invalid learning rate: (.+?)","errorType":"validation","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"timm/optim/adopt.py","lineNumber":89,"sourceCode":"            weight_decay: float = 0.0,\n            decoupled: bool = False,\n            corrected_weight_decay: bool = False,\n            *,\n            caution: bool = False,\n            foreach: Optional[bool] = False,\n            maximize: bool = False,\n            capturable: bool = False,\n            differentiable: bool = False,\n    ):\n        if isinstance(lr, Tensor):\n            if foreach and not capturable:\n                raise ValueError(\n                    \"lr as a Tensor is not supported for capturable=False and foreach=True\"\n                )\n            if lr.numel() != 1:\n                raise ValueError(\"Tensor lr must be 1-element\")\n        if not 0.0 <= lr:\n            raise ValueError(f\"Invalid learning rate: {lr}\")\n        if not 0.0 <= eps:\n            raise ValueError(f\"Invalid epsilon value: {eps}\")\n        if not 0.0 <= betas[0] < 1.0:\n            raise ValueError(f\"Invalid beta parameter at index 0: {betas[0]}\")\n        if not 0.0 <= betas[1] < 1.0:\n            raise ValueError(f\"Invalid beta parameter at index 1: {betas[1]}\")\n        if not 0.0 <= weight_decay:\n            raise ValueError(f\"Invalid weight_decay value: {weight_decay}\")\n\n        defaults = dict(\n            lr=lr,\n            betas=betas,\n            eps=eps,\n            weight_decay=weight_decay,\n            clip_exp=clip_exp,\n            decoupled=decoupled,\n            corrected_weight_decay=corrected_weight_decay,\n            caution=caution,","sourceCodeStart":71,"sourceCodeEnd":107,"githubUrl":"https://github.com/huggingface/pytorch-image-models/blob/9a5261e31b3b5128526eb2658333b4c0a54464ae/timm/optim/adopt.py#L71-L107","documentation":"Raised by ADOPT's constructor when the learning rate is negative (works for float lr via comparison, and tensor lr evaluates truthily per element context). The optimizer requires lr >= 0.","triggerScenarios":"Constructing timm.optim.Adopt(params, lr=-1e-3), or a config/sweep supplying a negative lr.","commonSituations":"Sign typo in config, hyperparameter search ranges crossing zero, or misparsed CLI scientific notation.","solutions":["Use a non-negative lr, typically 1e-3 for ADOPT","Check the config/sweep bounds and the value actually reaching the constructor"],"exampleFix":"# before\nopt = timm.optim.Adopt(model.parameters(), lr=-1e-3)\n# after\nopt = timm.optim.Adopt(model.parameters(), lr=1e-3)","handlingStrategy":"validation","validationCode":"lr_v = lr.item() if isinstance(lr, torch.Tensor) else lr\nassert lr_v >= 0.0, f'lr must be >= 0, got {lr_v}'","typeGuard":"def valid_lr(lr) -> bool:\n    v = lr.item() if isinstance(lr, torch.Tensor) else lr\n    return v >= 0.0","tryCatchPattern":null,"preventionTips":["Clamp sweep-sampled lr to positive values","Validate lr right after config load"],"tags":["optimizer","adopt","hyperparameters","validation"],"backgroundTag":"optimizer-hyperparameter-out-of-range","analyzedSha":"9a5261e31b3b5128526eb2658333b4c0a54464ae","analyzedAt":"2026-08-27T02:34:25.417Z","schemaVersion":2},"datasetVersion":"2026-08-27T03:17:27.898Z"}