想微调qwen3-vl-8b-instruct，但遇到 Processor was not found，请问现在llama-factory还不支持这个模型吗

[bigsmartbig]

`
W0119 12:17:29.398000 3822638 site-packages/torch/distributed/run.py:803]
W0119 12:17:29.398000 3822638 site-packages/torch/distributed/run.py:803] *****************************************
W0119 12:17:29.398000 3822638 site-packages/torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed.
W0119 12:17:29.398000 3822638 site-packages/torch/distributed/run.py:803] *****************************************
[W119 12:17:36.057487562 ProcessGroupNCCL.cpp:924] Warning: TORCH_NCCL_AVOID_RECORD_STREAMS is the default now, this environment variable is thus deprecated. (function operator())
[W119 12:17:36.130012532 ProcessGroupNCCL.cpp:924] Warning: TORCH_NCCL_AVOID_RECORD_STREAMS is the default now, this environment variable is thus deprecated. (function operator())
[INFO|tokenization_utils_base.py:2066] 2026-01-19 12:17:36,716 >> loading file vocab.json
[INFO|tokenization_utils_base.py:2066] 2026-01-19 12:17:36,717 >> loading file merges.txt
[INFO|tokenization_utils_base.py:2066] 2026-01-19 12:17:36,717 >> loading file tokenizer.json
[INFO|tokenization_utils_base.py:2066] 2026-01-19 12:17:36,717 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:2066] 2026-01-19 12:17:36,717 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:2066] 2026-01-19 12:17:36,717 >> loading file tokenizer_config.json
[INFO|tokenization_utils_base.py:2066] 2026-01-19 12:17:36,717 >> loading file chat_template.jinja
[INFO|tokenization_utils_base.py:2337] 2026-01-19 12:17:37,081 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
[INFO|image_processing_base.py:374] 2026-01-19 12:17:37,081 >> loading configuration file /data/model/Qwen3-VL-8B/preprocessor_config.json
[INFO|tokenization_utils_base.py:2066] 2026-01-19 12:17:37,082 >> loading file vocab.json
[INFO|tokenization_utils_base.py:2066] 2026-01-19 12:17:37,082 >> loading file merges.txt
[INFO|tokenization_utils_base.py:2066] 2026-01-19 12:17:37,082 >> loading file tokenizer.json
[INFO|tokenization_utils_base.py:2066] 2026-01-19 12:17:37,082 >> loading file added_tokens.json
[INFO|tokenization_utils_base.py:2066] 2026-01-19 12:17:37,082 >> loading file special_tokens_map.json
[INFO|tokenization_utils_base.py:2066] 2026-01-19 12:17:37,082 >> loading file tokenizer_config.json
[INFO|tokenization_utils_base.py:2066] 2026-01-19 12:17:37,083 >> loading file chat_template.jinja
[INFO|tokenization_utils_base.py:2337] 2026-01-19 12:17:37,459 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.

Converting format of dataset (num_proc=16): 0%| | 0/75 [00:00<?, ? examples/s]
Converting format of dataset (num_proc=16): 7%|▋ | 5/75 [00:00<00:09, 7.24 examples/s]
Converting format of dataset (num_proc=16): 100%|██████████| 75/75 [00:00<00:00, 124.93 examples/s]
Converting format of dataset (num_proc=16): 100%|██████████| 75/75 [00:00<00:00, 80.12 examples/s]
/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/torch/distributed/distributed_c10d.py:4876: UserWarning: barrier(): using the device under current context. You can specify device_id in init_process_group to mute this warning.
warnings.warn( # warn only once
[rank0]:[W119 12:17:39.512279645 ProcessGroupNCCL.cpp:5072] Guessing device ID based on global rank. This can cause a hang if rank to GPU mapping is heterogeneous. You can specify device_id in init_process_group()

Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:00<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:01<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:01<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:01<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:01<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:01<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:01<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:02<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:02<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:02<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:02<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:02<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:02<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:03<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:03<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:03<?, ? examples/s]
[rank0]: multiprocess.pool.RemoteTraceback:
[rank0]: """
[rank0]: Traceback (most recent call last):
[rank0]: File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/multiprocess/pool.py", line 125, in worker
[rank0]: result = (True, func(*args, **kwds))
[rank0]: ^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/datasets/utils/py_utils.py", line 586, in _write_generator_to_queue
[rank0]: for i, result in enumerate(func(**kwargs)):
[rank0]: File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3674, in _map_single
[rank0]: for i, batch in iter_outputs(shard_iterable):
[rank0]: File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3624, in iter_outputs
[rank0]: yield i, apply_function(example, i, offset=offset)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3547, in apply_function
[rank0]: processed_inputs = function(*fn_args, *additional_args, **fn_kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/user/LlamaFactory/src/llamafactory/data/processor/supervised.py", line 99, in preprocess_dataset
[rank0]: input_ids, labels = self._encode_data_example(
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/user/LlamaFactory/src/llamafactory/data/processor/supervised.py", line 43, in _encode_data_example
[rank0]: messages = self.template.mm_plugin.process_messages(prompt + response, images, videos, audios, self.processor)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/user/LlamaFactory/src/llamafactory/data/mm_plugin.py", line 1666, in process_messages
[rank0]: self._validate_input(processor, images, videos, audios)
[rank0]: File "/home/user/LlamaFactory/src/llamafactory/data/mm_plugin.py", line 181, in _validate_input
[rank0]: raise ValueError("Processor was not found, please check and update your model file.")
[rank0]: ValueError: Processor was not found, please check and update your model file.
[rank0]: """

[rank0]: The above exception was the direct cause of the following exception:

[rank0]: Traceback (most recent call last):
[rank0]: File "/home/user/LlamaFactory/src/llamafactory/launcher.py", line 185, in
[rank0]: run_exp()
[rank0]: File "/home/user/LlamaFactory/src/llamafactory/train/tuner.py", line 126, in run_exp
[rank0]: _training_function(config={"args": args, "callbacks": callbacks})
[rank0]: File "/home/user/LlamaFactory/src/llamafactory/train/tuner.py", line 88, in _training_function
[rank0]: run_sft(model_args, data_args, training_args, finetuning_args, generating_args, callbacks)
[rank0]: File "/home/user/LlamaFactory/src/llamafactory/train/sft/workflow.py", line 52, in run_sft
[rank0]: dataset_module = get_dataset(template, model_args, data_args, training_args, stage="sft", **tokenizer_module)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/user/LlamaFactory/src/llamafactory/data/loader.py", line 318, in get_dataset
[rank0]: train_dict["train"] = _get_preprocessed_dataset(
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/user/LlamaFactory/src/llamafactory/data/loader.py", line 255, in _get_preprocessed_dataset
[rank0]: dataset = dataset.map(
[rank0]: ^^^^^^^^^^^^
[rank0]: File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 560, in wrapper
[rank0]: out: Union["Dataset", "DatasetDict"] = func(self, *args, **kwargs)
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/datasets/arrow_dataset.py", line 3309, in map
[rank0]: for rank, done, content in iflatmap_unordered(
[rank0]: File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/datasets/utils/py_utils.py", line 626, in iflatmap_unordered
[rank0]: [async_result.get(timeout=0.05) for async_result in async_results]
[rank0]: File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/datasets/utils/py_utils.py", line 626, in
[rank0]: [async_result.get(timeout=0.05) for async_result in async_results]
[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
[rank0]: File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/multiprocess/pool.py", line 774, in get
[rank0]: raise self._value
[rank0]: ValueError: Processor was not found, please check and update your model file.

Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:00<?, ? examples/s][rank0]:[W119 12:17:44.453044828 ProcessGroupNCCL.cpp:1524] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())

Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:01<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:01<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:01<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:01<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:01<?, ? examples/s][rank0]:[W119 12:17:45.615043825 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())

Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:02<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:02<?, ? examples/s]
Running tokenizer on dataset (num_proc=16): 0%| | 0/75 [00:02<?, ? examples/s]W0119 12:17:46.404000 3822638 site-packages/torch/distributed/elastic/multiprocessing/api.py:908] Sending process 3822728 closing signal SIGTERM
E0119 12:17:46.921000 3822638 site-packages/torch/distributed/elastic/multiprocessing/api.py:882] failed (exitcode: 1) local_rank: 0 (pid: 3822727) of binary: /home/user/anaconda3/envs/llama-factory/bin/python3.11
Traceback (most recent call last):
File "/home/user/anaconda3/envs/llama-factory/bin/torchrun", line 7, in
sys.exit(main())
^^^^^^
File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/torch/distributed/elastic/multiprocessing/errors/init.py", line 357, in wrapper
return f(*args, **kwargs)
^^^^^^^^^^^^^^^^^^
File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/torch/distributed/run.py", line 936, in main
run(args)
File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/torch/distributed/run.py", line 927, in run
elastic_launch(
File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/torch/distributed/launcher/api.py", line 156, in call
return launch_agent(self._config, self._entrypoint, list(args))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/site-packages/torch/distributed/launcher/api.py", line 293, in launch_agent
raise ChildFailedError(
torch.distributed.elastic.multiprocessing.errors.ChildFailedError:

/home/user/LlamaFactory/src/llamafactory/launcher.py FAILED

Failures:
<NO_OTHER_FAILURES>

Root Cause (first observed failure):
[0]:
time : 2026-01-19_12:17:46
host : gpu
rank : 0 (local_rank: 0)
exitcode : 1 (pid: 3822727)
error_file: <N/A>
traceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html

[W119 12:17:47.954984534 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
Traceback (most recent call last):
File "/home/user/anaconda3/envs/llama-factory/bin/llamafactory-cli", line 7, in
sys.exit(main())
^^^^^^
File "/home/user/LlamaFactory/src/llamafactory/cli.py", line 24, in main
launcher.launch()
File "/home/user/LlamaFactory/src/llamafactory/launcher.py", line 115, in launch
process = subprocess.run(
^^^^^^^^^^^^^^^
File "/home/user/anaconda3/envs/llama-factory/lib/python3.11/subprocess.py", line 571, in run
raise CalledProcessError(retcode, process.args,
subprocess.CalledProcessError: Command '['torchrun', '--nnodes', '1', '--node_rank', '0', '--nproc_per_node', '2', '--master_addr', '127.0.0.1', '--master_port', '37243', '/home/user/LlamaFactory/src/llamafactory/launcher.py', 'saves/Qwen3-VL-8B-Instruct/lora/train_2026-01-19-11-46-59/training_args.yaml']' returned non-zero exit status 1.
[W119 12:17:48.995677198 AllocatorConfig.cpp:28] Warning: PYTORCH_CUDA_ALLOC_CONF is deprecated, use PYTORCH_ALLOC_CONF instead (function operator())
`

[bigsmartbig]

image标签的问题
标签数量跟传入的图片数量对不上