{"record":{"id":"b0e02ba1966204a7","repo":"huggingface/pytorch-image-models","slug":"invalid-beta-parameter-at-index-0-b0e02b","errorCode":null,"errorMessage":"Invalid beta parameter at index 0: {}","messagePattern":"Invalid beta parameter at index 0: (.+?)","errorType":"validation","errorClass":"ValueError","httpStatus":null,"severity":"error","filePath":"timm/optim/adan.py","lineNumber":78,"sourceCode":"        foreach: If True would use torch._foreach implementation. Faster but uses slightly more memory.\n    \"\"\"\n\n    def __init__(self,\n            params,\n            lr: float = 1e-3,\n            betas: Tuple[float, float, float] = (0.98, 0.92, 0.99),\n            eps: float = 1e-8,\n            weight_decay: float = 0.0,\n            no_prox: bool = False,\n            caution: bool = False,\n            foreach: Optional[bool] = None,\n    ):\n        if not 0.0 <= lr:\n            raise ValueError('Invalid learning rate: {}'.format(lr))\n        if not 0.0 <= eps:\n            raise ValueError('Invalid epsilon value: {}'.format(eps))\n        if not 0.0 <= betas[0] < 1.0:\n            raise ValueError('Invalid beta parameter at index 0: {}'.format(betas[0]))\n        if not 0.0 <= betas[1] < 1.0:\n            raise ValueError('Invalid beta parameter at index 1: {}'.format(betas[1]))\n        if not 0.0 <= betas[2] < 1.0:\n            raise ValueError('Invalid beta parameter at index 2: {}'.format(betas[2]))\n\n        defaults = dict(\n            lr=lr,\n            betas=betas,\n            eps=eps,\n            weight_decay=weight_decay,\n            no_prox=no_prox,\n            caution=caution,\n            foreach=foreach,\n        )\n        super().__init__(params, defaults)\n\n    def __setstate__(self, state):\n        super(Adan, self).__setstate__(state)","sourceCodeStart":60,"sourceCodeEnd":96,"githubUrl":"https://github.com/huggingface/pytorch-image-models/blob/9a5261e31b3b5128526eb2658333b4c0a54464ae/timm/optim/adan.py#L60-L96","documentation":"Raised by timm's Adan optimizer constructor when betas[0] (beta1, first-moment decay) is outside [0.0, 1.0). Adan uses three decay coefficients; all must be in [0, 1) for the bias-corrected moment estimates to be well-defined.","triggerScenarios":"Calling timm.optim.Adan(params, betas=(b1, b2, b3)) where b1 < 0.0 or b1 >= 1.0, e.g. betas=(1.0, 0.99, 0.9).","commonSituations":"Config typo in the three-element betas tuple, or copying two-element Adam betas plus appending an out-of-range third value incorrectly; reordered tuple values.","solutions":["Set beta1 in [0.0, 1.0), typically 0.98 for Adan","Verify the betas tuple has three values in the order (beta1, beta2, beta3)","Check the YAML/CLI for typos in the betas list"],"exampleFix":"# before\nopt = timm.optim.Adan(model.parameters(), lr=1e-3, betas=(9.8, 0.99, 0.9))\n# after\nopt = timm.optim.Adan(model.parameters(), lr=1e-3, betas=(0.98, 0.92, 0.99))","handlingStrategy":"validation","validationCode":"assert len(betas) == 3 and all(0.0 <= b < 1.0 for b in betas), f'betas out of range: {betas}'","typeGuard":"def valid_adan_betas(betas: tuple) -> bool:\n    return len(betas) == 3 and all(isinstance(b, (int, float)) and 0.0 <= b < 1.0 for b in betas)","tryCatchPattern":null,"preventionTips":["Remember Adan takes THREE betas unlike Adam's two","Validate tuple length and range together","Use the paper defaults (0.98, 0.92, 0.99) unless tuned"],"tags":["optimizer","adan","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"}