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This is a beta version of the video understanding. It may not work as expected.
Fetching 12 files: 100%|████████████████| 12/12 [00:00<00:00, 136400.13it/s]
Using a slow image processor as `use_fast` is unset and a slow processor was saved with this model. `use_fast=True` will be the default behavior in v4.48, even if the model was saved with a slow processor. This will result in minor differences in outputs. You'll still be able to use a slow processor with `use_fast=False`.
Traceback (most recent call last):
File "/opt/homebrew/Cellar/python@3.10/3.10.16/Frameworks/Python.framework/Versions/3.10/lib/python3.10/runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "/opt/homebrew/Cellar/python@3.10/3.10.16/Frameworks/Python.framework/Versions/3.10/lib/python3.10/runpy.py", line 86, in _run_code
exec(code, run_globals)
File "/Users/leoho/Desktop/mlx-llm-example/.venv/lib/python3.10/site-packages/mlx_vlm/generate.py", line 156, in <module>
main()
File "/Users/leoho/Desktop/mlx-llm-example/.venv/lib/python3.10/site-packages/mlx_vlm/generate.py", line 97, in main
model, processor, config = get_model_and_processors(args.model, args.adapter_path)
File "/Users/leoho/Desktop/mlx-llm-example/.venv/lib/python3.10/site-packages/mlx_vlm/generate.py", line 86, in get_model_and_processors
model, processor = load(
File "/Users/leoho/Desktop/mlx-llm-example/.venv/lib/python3.10/site-packages/mlx_vlm/utils.py", line 279, in load
processor = load_processor(model_path, True, **kwargs)
File "/Users/leoho/Desktop/mlx-llm-example/.venv/lib/python3.10/site-packages/mlx_vlm/utils.py", line 342, in load_processor
processor = AutoProcessor.from_pretrained(model_path, **kwargs)
File "/Users/leoho/Desktop/mlx-llm-example/.venv/lib/python3.10/site-packages/transformers/models/auto/processing_auto.py", line 334, in from_pretrained
return processor_class.from_pretrained(
File "/Users/leoho/Desktop/mlx-llm-example/.venv/lib/python3.10/site-packages/transformers/processing_utils.py", line 1070, in from_pretrained
args = cls._get_arguments_from_pretrained(pretrained_model_name_or_path, **kwargs)
File "/Users/leoho/Desktop/mlx-llm-example/.venv/lib/python3.10/site-packages/transformers/processing_utils.py", line 1116, in _get_arguments_from_pretrained
args.append(attribute_class.from_pretrained(pretrained_model_name_or_path, **kwargs))
File "/Users/leoho/Desktop/mlx-llm-example/.venv/lib/python3.10/site-packages/transformers/models/auto/image_processing_auto.py", line 569, in from_pretrained
raise ValueError(
ValueError: Unrecognized image processor in /Users/leoho/.cache/huggingface/hub/models--mlx-community--Qwen2.5-VL-7B-Instruct-4bit/snapshots/75c400d442a81dc0c5ef90f095d8815112ecd350. Should have a `image_processor_type` key in its preprocessor_config.json of config.json, or one of the following `model_type` keys in its config.json: align, aria, beit, bit, blip, blip-2, bridgetower, chameleon, chinese_clip, clip, clipseg, conditional_detr, convnext, convnextv2, cvt, data2vec-vision, deformable_detr, deit, depth_anything, depth_pro, deta, detr, dinat, dinov2, donut-swin, dpt, efficientformer, efficientnet, flava, focalnet, fuyu, git, glpn, got_ocr2, grounding-dino, groupvit, hiera, idefics, idefics2, idefics3, ijepa, imagegpt, instructblip, instructblipvideo, kosmos-2, layoutlmv2, layoutlmv3, levit, llava, llava_next, llava_next_video, llava_onevision, mask2former, maskformer, mgp-str, mllama, mobilenet_v1, mobilenet_v2, mobilevit, mobilevitv2, nat, nougat, oneformer, owlv2, owlvit, paligemma, perceiver, pix2struct, pixtral, poolformer, pvt, pvt_v2, qwen2_5_vl, qwen2_vl, regnet, resnet, rt_detr, sam, segformer, seggpt, siglip, superglue, swiftformer, swin, swin2sr, swinv2, table-transformer, timesformer, timm_wrapper, tvlt, tvp, udop, upernet, van, videomae, vilt, vipllava, vit, vit_hybrid, vit_mae, vit_msn, vitmatte, xclip, yolos, zoedepth
Hello, I noticed the same behaviour today as well. Before that everything was working as expected. I think the problem is with the requirement transformers>=4.49.0.
When I use the sample code from HuggingFace Model card
I get the following error ( mlx-vlm.log )
How can I solve this problem?
Environment
Hardware
Software
requirements.txt
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