auto-round Inference failure in the Qwen3 Omni quantized model

Autoround cannot load the model from local path.

If the path is from huggingface, it loads.

If the path is from local, it gives the error:


auto_round/utils.py", line 1610, in mllm_load_model
model = model.eval()
UnboundLocalError: local variable 'model' referenced before assignment.

If you can hint the issue.

Sep 29 '25 11:09 Tortoise17

Thanks for reporting this issue, may I know your cmd and auto-round version? I just tried the main branch, it worked fine with the following test

from auto_round import AutoRound
ar = AutoRound("/models/Qwen2.5-VL-7B-Instruct",iters=1,nsamples=1)
ar.quantize_and_save()

Sep 29 '25 12:09 wenhuach21

There may be some bugs when loading the model, and AutoRound incorrectly swallows the exception. I’d appreciate it if you could provide the command you used to reproduce this issue

Sep 29 '25 12:09 wenhuach21

try this pr to check if any exception has been raised when loading your model https://github.com/intel/auto-round/pull/863

Sep 29 '25 12:09 wenhuach21

@wenhuach21

from auto_round import AutoRound

model_name_or_path = "Qwen3-Omni-30B-A3B-Instruct"

ar = AutoRound(
    model=model_name_or_path,
    scheme="W4A16",
    iters=50,
    lr=5e-3,
)

output_dir = "./tmp_autoround" ar.quantize_and_save(output_dir, format="auto_round")

This was command but, pr #863 has resolved it. So far it looks. I am trying next and looking if the things finish until the completion with success. Thank you again.

Sep 29 '25 15:09 Tortoise17

@wenhuach21 The next error I am facing is below.

2025-09-29 18:36:27 INFO autoround.py L146: using MLLM mode for multimodal model.
2025-09-29 18:36:28 WARNING modeling_utils.py L4569: `torch_dtype` is deprecated! Use `dtype` instead!
Unrecognized keys in `rope_scaling` for 'rope_type'='default': {'interleaved', 'mrope_section', 'mrope_interleaved'}
Loading checkpoint shards: 100%|█████████████████████████████████████████████████| 16/16 [01:39<00:00,  6.25s/it]
2025-09-29 18:38:11 WARNING configuration_utils.py L692: The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
2025-09-29 18:38:12 WARNING image_processing_auto.py L532: The image processor of type `Qwen2VLImageProcessor` is now loaded as a fast processor by default, even if the model checkpoint was saved with a slow processor. This is a breaking change and may produce slightly different outputs. To continue using the slow processor, instantiate this class with `use_fast=False`. Note that this behavior will be extended to all models in a future release.
2025-09-29 18:38:13,477 INFO config.py L54: PyTorch version 2.5.1+cu124 available.
2025-09-29 18:38:53 WARNING compressor.py L218: qwen3_omni_moe does not support for NeelNanda/pile-10k, will use liuhaotian/llava_conv_58k with default config as an alternative.
2025-09-29 18:38:53 WARNING compressor.py L228: reset batch_size(8) to 1 and gradient_accumulate_steps(1) to 8, because batch_size=8 cannot be used for liuhaotian/llava_conv_58k
2025-09-29 18:38:54 INFO base.py L369: using torch.bfloat16 for quantization tuning
2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.0.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq

This might be because either the autoround or the transformer has no support for Qwen3-Omni ?

Sep 29 '25 16:09 Tortoise17

@wenhuach21 The next error I am facing is below.

2025-09-29 18:36:27 INFO autoround.py L146: using MLLM mode for multimodal model.
2025-09-29 18:36:28 WARNING modeling_utils.py L4569: `torch_dtype` is deprecated! Use `dtype` instead!
Unrecognized keys in `rope_scaling` for 'rope_type'='default': {'interleaved', 'mrope_section', 'mrope_interleaved'}
Loading checkpoint shards: 100%|█████████████████████████████████████████████████| 16/16 [01:39<00:00,  6.25s/it]
2025-09-29 18:38:11 WARNING configuration_utils.py L692: The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.
2025-09-29 18:38:12 WARNING image_processing_auto.py L532: The image processor of type `Qwen2VLImageProcessor` is now loaded as a fast processor by default, even if the model checkpoint was saved with a slow processor. This is a breaking change and may produce slightly different outputs. To continue using the slow processor, instantiate this class with `use_fast=False`. Note that this behavior will be extended to all models in a future release.
2025-09-29 18:38:13,477 INFO config.py L54: PyTorch version 2.5.1+cu124 available.
2025-09-29 18:38:53 WARNING compressor.py L218: qwen3_omni_moe does not support for NeelNanda/pile-10k, will use liuhaotian/llava_conv_58k with default config as an alternative.
2025-09-29 18:38:53 WARNING compressor.py L228: reset batch_size(8) to 1 and gradient_accumulate_steps(1) to 8, because batch_size=8 cannot be used for liuhaotian/llava_conv_58k
2025-09-29 18:38:54 INFO base.py L369: using torch.bfloat16 for quantization tuning
2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.0.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq

This might be because either the autoround or the transformer has no support for Qwen3-Omni ?

There are no exceptions in this log. @n1ck-guo has tried this before, though quantization could be conducted successfully, inference has some issues which have not been root caused yet.

Sep 30 '25 01:09 wenhuach21

@wenhuach21 yes, that exception has been given. Due to that reason the process started for the pickup of the checkpoints. The below is now complete log.

2025-09-29 18:36:27 INFO autoround.py L146: using MLLM mode for multimodal model. 2025-09-29 18:36:28 WARNING modeling_utils.py L4569: torch_dtypeis deprecated! Usedtypeinstead! Unrecognized keys inrope_scalingfor 'rope_type'='default': {'interleaved', 'mrope_section', 'mrope_interleaved'} Loading checkpoint shards: 100%|█████████████████████████████████████████████████| 16/16 [01:39<00:00, 6.25s/it] 2025-09-29 18:38:11 WARNING configuration_utils.py L692: The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. SetTRANSFORMERS_VERBOSITY=infofor more details. 2025-09-29 18:38:12 WARNING image_processing_auto.py L532: The image processor of typeQwen2VLImageProcessoris now loaded as a fast processor by default, even if the model checkpoint was saved with a slow processor. This is a breaking change and may produce slightly different outputs. To continue using the slow processor, instantiate this class withuse_fast=False. Note that this behavior will be extended to all models in a future release. 2025-09-29 18:38:13,477 INFO config.py L54: PyTorch version 2.5.1+cu124 available. 2025-09-29 18:38:53 WARNING compressor.py L218: qwen3_omni_moe does not support for NeelNanda/pile-10k, will use liuhaotian/llava_conv_58k with default config as an alternative. 2025-09-29 18:38:53 WARNING compressor.py L228: reset batch_size(8) to 1 and gradient_accumulate_steps(1) to 8, because batch_size=8 cannot be used for liuhaotian/llava_conv_58k 2025-09-29 18:38:54 INFO base.py L369: using torch.bfloat16 for quantization tuning 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.0.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.0.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.1.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.1.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.2.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.2.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.3.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.3.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.4.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.4.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.5.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.5.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.6.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.6.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.7.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.7.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.8.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.8.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.9.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.9.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.10.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.10.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.11.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.11.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.12.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.12.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.13.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.13.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.14.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.14.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.15.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.15.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.16.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.16.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.17.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.17.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.18.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.18.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.19.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.19.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.20.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.20.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.21.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.21.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.22.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.22.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.23.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.23.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.24.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.24.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.25.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.25.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.26.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:38:54 INFO base.py L769: thinker.visual.blocks.26.mlp.linear_fc2 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq 2025-09-29 18:39:03 INFO base.py L1765: start to cache block inputs 2025-09-29 18:39:04 INFO base.py L2279: switch to cpu to cache block inputs 2025-09-29 18:39:07 INFO dataset.py L95: use dataset llava_conv_58k, downloading ... cache block inputs: 0%| | 0/512 [00:00<?, ?it/s]2025-09-29 18:39:21,163 INFO _client.py L1025: HTTP Request: GET http://images.cocodataset.org/train2017/000000183280.jpg "HTTP/1.1 200 OK" cache block inputs: 0%| | 1/512 [00:00<08:25, 1.01it/s]2025-09-29 18:39:22,079 INFO _client.py L1025: HTTP Request: GET http://images.cocodataset.org/train2017/000000168356.jpg "HTTP/1.1 200 OK" cache block inputs: 0%|▏ | 2/512 [00:01<06:32, 1.30it/s]2025-09-29 18:39:22,666 INFO _client.py L1025: HTTP Request: GET http://images.cocodataset.org/train2017/000000456845.jpg "HTTP/1.1 200 OK" cache block inputs: 1%|▎ | 3/512 [00:02<05:50, 1.45it/s]2025-09-29 18:39:23,290 INFO _client.py L1025: HTTP Request: GET http://images.cocodataset.org/train2017/000000455081.jpg "HTTP/1.1 200 OK" cache block inputs: 1%|▍ | 4/512 [00:03<06:13, 1.36it/s]2025-09-29 18:39:24,067 INFO _client.py L1025: HTTP Request: GET 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Sep 30 '25 05:09 Tortoise17

set the seqlen to 512 and retry.

Sep 30 '25 05:09 wenhuach21

This issue is similar to #866. As we’ll be on holiday, the fix may be a bit delayed and is expected no earlier than 10/8.

Sep 30 '25 05:09 wenhuach21

seqlen should be set at which stage?

ar = AutoRound(
    model=model_name_or_path,
    scheme="W4A16",
    iters=50,
    lr=5e-3,
seqlen=512,
)

like this didnt work

Sep 30 '25 05:09 Tortoise17

Sorry, I have no time to debug this issue today. @n1ck-guo could you take a look after the holiday?

Sep 30 '25 08:09 wenhuach21

@wenhuach21 @n1ck-guo in my tests, RTN MODE for exporting autoround format works so far. But, while exporting, there is error or log warning 2025-09-30 10:24:09 INFO base.py L751: thinker.visual.blocks.0.mlp.linear_fc1 will not be quantized due to its shape not being divisible by 32, resulting in an exporting issue to autogptq

and I don't know if the exported weights are still correct.

Remaining all the possibilities are no more possible.

Sep 30 '25 08:09 Tortoise17

You can safely ignore this warning. It simply indicates that some layers are being reverted to 16-bit precision, since quantizing them could cause inference issues. However, Heng mentioned earlier that the inference problems are definitely not related to this.

Sep 30 '25 08:09 wenhuach21

Hi, anybody successfully quantized qwen3 omni?

Dec 01 '25 06:12 allerou4

sorry, we have no update for omni, it still has some inference issue currently. We will take a further look when we are free.

Dec 01 '25 08:12 wenhuach21

@wenhuach21 @allerou4 Qwen3-Omni Quantized Here is the link for the Qwen3-Omni. But, I have found out that with auto-round pipelines are more stable than other mode of techniques. I am big fan of auto-round.

Dec 01 '25 08:12 Tortoise17

@wenhuach21 @allerou4 Qwen3-Omni Quantized Here is the link for the Qwen3-Omni. But, I have found out that with auto-round pipelines are more stable than other mode of techniques. I am big fan of auto-round.

Yeah, I tried this one, as we find that sometimes it will output repeating tokens, so we decide to do it on our own.

Dec 01 '25 09:12 allerou4

@allerou4 memory wise that is not stable as well. vllm and transformers are also culprit.

Dec 01 '25 09:12 Tortoise17