huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/data/workspace/llm/weight_files/Video-LLaVA-7B'. Use repo_type argument if needed.

[HAOYON-666]

When I use this code to load the local model for inference, it doesn't work properly

import torch
from videollava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
from videollava.conversation import conv_templates, SeparatorStyle
from videollava.model.builder import load_pretrained_model
from videollava.utils import disable_torch_init
from videollava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria

def main():
    disable_torch_init()
    image = './videollava/serve/examples/extreme_ironing.jpg'
    inp = 'What is unusual about this image?'
    model_path = '/data/workspace/llm/weight_files/Video-LLaVA-7B'
    cache_dir = '/data/workspace/llm/weight_files/Video-LLaVA-7B'
    device = 'cuda'
    load_4bit, load_8bit = True, False
    model_name = get_model_name_from_path(model_path)
    tokenizer, model, processor, _ = load_pretrained_model(model_path, None, model_name, load_8bit, load_4bit, device=device, cache_dir=cache_dir)
    image_processor = processor['image']
    conv_mode = "llava_v1"
    conv = conv_templates[conv_mode].copy()
    roles = conv.roles

    image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values']
    if type(image_tensor) is list:
        tensor = [image.to(model.device, dtype=torch.float16) for image in image_tensor]
    else:
        tensor = image_tensor.to(model.device, dtype=torch.float16)

    print(f"{roles[1]}: {inp}")
    inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
    conv.append_message(conv.roles[0], inp)
    conv.append_message(conv.roles[1], None)
    prompt = conv.get_prompt()
    input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).cuda()
    stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2
    keywords = [stop_str]
    stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)

    with torch.inference_mode():
        output_ids = model.generate(
            input_ids,
            images=tensor,
            do_sample=True,
            temperature=0.2,
            max_new_tokens=1024,
            use_cache=True,
            stopping_criteria=[stopping_criteria])

    outputs = tokenizer.decode(output_ids[0, input_ids.shape[1]:]).strip()
    print(outputs)

if __name__ == '__main__':
    main()

[HAOYON-666]

/home/user/miniconda3/envs/videollava/lib/python3.10/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead.
warnings.warn(
/home/user/miniconda3/envs/videollava/lib/python3.10/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead.
warnings.warn(
/home/user/miniconda3/envs/videollava/lib/python3.10/site-packages/torchvision/transforms/functional_tensor.py:5: UserWarning: The torchvision.transforms.functional_tensor module is deprecated in 0.15 and will be removed in 0.17. Please don't rely on it. You probably just need to use APIs in torchvision.transforms.functional or in torchvision.transforms.v2.functional.
warnings.warn(
Traceback (most recent call last):
File "/data/workspace/zhaoyong/model/Video-LLaVA/infer.py", line 107, in
main()
File "/data/workspace/zhaoyong/model/Video-LLaVA/infer.py", line 71, in main
tokenizer, model, processor, _ = load_pretrained_model(model_path, None, model_name, load_8bit, load_4bit, device=device, cache_dir=cache_dir)
File "/data/workspace/zhaoyong/model/Video-LLaVA/videollava/model/builder.py", line 107, in load_pretrained_model
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
File "/home/user/miniconda3/envs/videollava/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py", line 652, in from_pretrained
tokenizer_config = get_tokenizer_config(pretrained_model_name_or_path, **kwargs)
File "/home/user/miniconda3/envs/videollava/lib/python3.10/site-packages/transformers/models/auto/tokenization_auto.py", line 496, in get_tokenizer_config
resolved_config_file = cached_file(
File "/home/user/miniconda3/envs/videollava/lib/python3.10/site-packages/transformers/utils/hub.py", line 417, in cached_file
resolved_file = hf_hub_download(
File "/home/user/miniconda3/envs/videollava/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 106, in _inner_fn
validate_repo_id(arg_value)
File "/home/user/miniconda3/envs/videollava/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py", line 154, in validate_repo_id
raise HFValidationError(
huggingface_hub.errors.HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/data/workspace/zhaoyong/llm/weight_files/Video-LLaVA-7B'. Use repo_type argument if needed.