---
title: "QLinearWoQ"
sidebar_label: "QLinearWoQ"
description: "The clika_runtime.nn QLinearWoQ class."
---

<!-- Generated by tools/api_reference/generate_api_docs.py. Do not edit. -->

QLinearWoQ(in_features, out_features, bias=True, device=None, dtype=None, *, activation=None)

    Weight-only-quantized linear: ``y = x @ dequant(weight).T + bias`` with
    the weight resting quantized. A quantized payload binds as is (its
    scheme travels with it); a dense payload is refused.

    Args:
        in_features: the size of each input sample.
        out_features: the size of each output sample.
        bias: declare the (floating-point) bias slot.
        device: where the layer's tensors live; ``None`` declares on the
            cpu.
        dtype: the activation dtype the layer computes in; ``None`` adopts
            the bound payload's logical dtype.
        activation: a fused epilogue applied to every output (a name such
            as ``"silu"`` or an :class:`~clika_runtime.Activation`
            value); ``None`` for none. How the bound weight lives at rest
            is the ``weight_residency`` option of the load that binds it.
    

## `__init__`

```python
__init__(self, in_features: 'int', out_features: 'int', bias: 'bool' = True, device: 'Device | str | None' = None, dtype: 'DtypeLike | None' = None, *, activation: 'ActivationLike' = None) -> 'None'
```

Initialize self.  See help(type(self)) for accurate signature.

## `extra_repr`

```python
extra_repr(self) -> 'str'
```

extra_repr() -> str

One line of per-class detail for :meth:`__repr__`; a layer prints
its geometry here (``in_features=64, out_features=256``).

## `forward`

```python
forward(self, input: 'Tensor') -> 'Tensor'
```

forward(input) -> Tensor

Floating-point ``[*, in_features]`` to ``[*, out_features]``.

## `from_weights`

```python
from_weightsfrom_weights(weight, bias=None, *, activation=None) -> QLinearWoQ
```

from_weights(weight, bias=None, *, activation=None) -> QLinearWoQ

Construct FROM a quantized weight (``crt.ops.quantize``'s
QTensor): geometry, dtype, and device read off the payload; an
optional ``bias`` binds beside it.

## `set_weights`

```python
set_weights(self, weight: 'QTensor', bias: 'Tensor | None' = None) -> 'None'
```

set_weights(weight, bias=None) -> None

Bind the quantized weight (its scheme travels with it) and the
optional floating-point bias; a dense weight is refused.