{"record":{"id":"9c4a6fe06da5e256","repo":"huggingface/pytorch-image-models","slug":"invalid-beta-parameter-at-index-0-betas-0-9c4a6f","errorCode":null,"errorMessage":"Invalid beta parameter at index 0: {betas[0]}","messagePattern":"Invalid beta parameter at index 0: (.+?)","errorType":"validation","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"timm/optim/adopt.py","lineNumber":93,"sourceCode":"            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,\n            capturable=capturable,\n            differentiable=differentiable,","sourceCodeStart":75,"sourceCodeEnd":111,"githubUrl":"https://github.com/huggingface/pytorch-image-models/blob/9a5261e31b3b5128526eb2658333b4c0a54464ae/timm/optim/adopt.py#L75-L111","documentation":"Raised by ADOPT's constructor when betas[0] (beta1) is outside [0.0, 1.0). ADOPT's moment estimates require decay coefficients strictly below 1 to converge.","triggerScenarios":"Constructing timm.optim.Adopt(params, betas=(b1, b2)) with b1 < 0.0 or b1 >= 1.0, e.g. betas=(1.0, 0.99) or betas=(0.9*10, 0.99).","commonSituations":"Typo in a two-element betas tuple, or copying a paper's hyperparameters with the decimal point dropped.","solutions":["Set beta1 in [0.0, 1.0), typically 0.9","Verify betas tuple order (beta1, beta2) and values in your config"],"exampleFix":"# before\nopt = timm.optim.Adopt(model.parameters(), lr=1e-3, betas=(9.0, 0.99))\n# after\nopt = timm.optim.Adopt(model.parameters(), lr=1e-3, betas=(0.9, 0.99))","handlingStrategy":"validation","validationCode":"assert len(betas) == 2 and all(0.0 <= b < 1.0 for b in betas), f'betas out of range: {betas}'","typeGuard":"def valid_adopt_betas(betas: tuple) -> bool:\n    return len(betas) == 2 and all(isinstance(b, (int, float)) and 0.0 <= b < 1.0 for b in betas)","tryCatchPattern":null,"preventionTips":["Validate betas before optimizer construction","ADOPT defaults (0.9, 0.99) are usually fine; only change deliberately"],"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"}