Q6_K gguf quant not working

Nobody has claimed this yet.

Get the following error when starting inference:

`Traceback (most recent call last):
File "H:\forge\webui\modules_forge\main_thread.py", line 30, in work
self.result = self.func(*self.args, **self.kwargs)
File "H:\forge\webui\modules\txt2img.py", line 110, in txt2img_function
processed = processing.process_images(p)
File "H:\forge\webui\modules\processing.py", line 813, in process_images
res = process_images_inner(p)
File "H:\forge\webui\modules\processing.py", line 956, in process_images_inner
samples_ddim = p.sample(conditioning=p.c, unconditional_conditioning=p.uc, seeds=p.seeds, subseeds=p.subseeds, subseed_strength=p.subseed_strength, prompts=p.prompts)
File "H:\forge\webui\modules\processing.py", line 1327, in sample
samples = self.sampler.sample(self, x, conditioning, unconditional_conditioning, image_conditioning=self.txt2img_image_conditioning(x))
File "H:\forge\webui\modules\sd_samplers_kdiffusion.py", line 234, in sample
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
File "H:\forge\webui\modules\sd_samplers_common.py", line 272, in launch_sampling
return func()
File "H:\forge\webui\modules\sd_samplers_kdiffusion.py", line 234, in
samples = self.launch_sampling(steps, lambda: self.func(self.model_wrap_cfg, x, extra_args=self.sampler_extra_args, disable=False, callback=self.callback_state, **extra_params_kwargs))
File "H:\forge\system\python\lib\site-packages\torch\utils\_contextlib.py", line 116, in decorate_context
return func(*args, **kwargs)
File "H:\forge\webui\k_diffusion\sampling.py", line 128, in sample_euler
denoised = model(x, sigma_hat * s_in, **extra_args)
File "H:\forge\system\python\lib\site-packages\torch\nn\modules\module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "H:\forge\system\python\lib\site-packages\torch\nn\modules\module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "H:\forge\webui\modules\sd_samplers_cfg_denoiser.py", line 186, in forward
denoised, cond_pred, uncond_pred = sampling_function(self, denoiser_params=denoiser_params, cond_scale=cond_scale, cond_composition=cond_composition)
File "H:\forge\webui\backend\sampling\sampling_function.py", line 339, in sampling_function
denoised, cond_pred, uncond_pred = sampling_function_inner(model, x, timestep, uncond, cond, cond_scale, model_options, seed, return_full=True)
File "H:\forge\webui\backend\sampling\sampling_function.py", line 284, in sampling_function_inner
cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)
File "H:\forge\webui\backend\sampling\sampling_function.py", line 254, in calc_cond_uncond_batch
output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
File "H:\forge\webui\backend\modules\k_model.py", line 45, in apply_model
model_output = self.diffusion_model(xc, t, context=context, control=control, transformer_options=transformer_options, **extra_conds).float()
File "H:\forge\system\python\lib\site-packages\torch\nn\modules\module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "H:\forge\system\python\lib\site-packages\torch\nn\modules\module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "H:\forge\webui\backend\nn\flux.py", line 402, in forward
out = self.inner_forward(img, img_ids, context, txt_ids, timestep, y, guidance)
File "H:\forge\webui\backend\nn\flux.py", line 373, in inner_forward
img, txt = block(img=img, txt=txt, vec=vec, pe=pe)
File "H:\forge\system\python\lib\site-packages\torch\nn\modules\module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "H:\forge\system\python\lib\site-packages\torch\nn\modules\module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "H:\forge\webui\backend\nn\flux.py", line 191, in forward
img_mod1_shift, img_mod1_scale, img_mod1_gate, img_mod2_shift, img_mod2_scale, img_mod2_gate = self.img_mod(vec)
File "H:\forge\system\python\lib\site-packages\torch\nn\modules\module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "H:\forge\system\python\lib\site-packages\torch\nn\modules\module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "H:\forge\webui\backend\nn\flux.py", line 161, in forward
out = self.lin(nn.functional.silu(vec))[:, None, :].chunk(self.multiplier, dim=-1)
File "H:\forge\system\python\lib\site-packages\torch\nn\modules\module.py", line 1553, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "H:\forge\system\python\lib\site-packages\torch\nn\modules\module.py", line 1562, in _call_impl
return forward_call(*args, **kwargs)
File "H:\forge\webui\backend\operations.py", line 369, in forward
return functional_linear_gguf(x, self.weight, self.bias)
File "H:\forge\webui\backend\operations_gguf.py", line 60, in functional_linear_gguf
return torch.nn.functional.linear(x, weight, bias)
RuntimeError: mat1 and mat2 shapes cannot be multiplied (1x3072 and 2520x18432)
mat1 and mat2 shapes cannot be multiplied (1x3072 and 2520x18432)`

Start with the traceback entry points in backend/nn/flux.py, backend/operations.py, and backend/operations_gguf.py, then reproduce the Q6_K inference failure. Trace the tensor dimensions reaching functional_linear_gguf and identify the relevant model or quantization mismatch; done means Q6_K inference completes without the reported shape error.

Written by the indexing model from the issue text.