{"record":{"id":"7d02ca9a7ab74cc5","repo":"huggingface/pytorch-image-models","slug":"invalid-epsilon-value-eps-7d02ca","errorCode":null,"errorMessage":"Invalid epsilon value: {eps}","messagePattern":"Invalid epsilon value: (.+?)","errorType":"validation","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"timm/optim/adopt.py","lineNumber":91,"sourceCode":"            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,\n            maximize=maximize,\n            foreach=foreach,","sourceCodeStart":73,"sourceCodeEnd":109,"githubUrl":"https://github.com/huggingface/pytorch-image-models/blob/9a5261e31b3b5128526eb2658333b4c0a54464ae/timm/optim/adopt.py#L73-L109","documentation":"Raised by ADOPT's constructor when eps is negative. eps is added to denominators for numerical stability and must be >= 0.","triggerScenarios":"Constructing timm.optim.Adopt(params, eps=-1e-8).","commonSituations":"Config typo on the eps key, or a sweep over log-spaced eps crossing zero.","solutions":["Use a small non-negative eps, typically 1e-8","Fix the YAML/CLI value bound to eps"],"exampleFix":"# before\nopt = timm.optim.Adopt(model.parameters(), lr=1e-3, eps=-1e-8)\n# after\nopt = timm.optim.Adopt(model.parameters(), lr=1e-3, eps=1e-8)","handlingStrategy":"validation","validationCode":"assert eps >= 0.0, f'eps must be >= 0, got {eps}'","typeGuard":"def valid_eps(eps) -> bool:\n    return isinstance(eps, (int, float)) and eps >= 0.0","tryCatchPattern":null,"preventionTips":["Keep eps at the 1e-8 default","Validate optimizer scalars centrally in config loading"],"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"}