OSError: /repository does not appear to have a file named config.json. Checkout 'https://huggingface.co//repository/None' for available files
I get the following error when trying to deploy an endpoint from : radames/stable-diffusion-2-1-unclip-img2imgOSError: /repository does not appear to have a file named config.json. Checkout ‘https://huggingface.co//repository/None’ for available files.
2023-08-02T06:45:09Z
Hi@aifreelanceris the query of yours resolved?If not@philschmid, can you help us out in this case, even i’m getting the same issue when i load “google/flan-ul2” model on Sagemaker
2023-08-02T06:47:45Z
I was running my experiment on a ml.g5.xlarge sagemaker notebook instance with Volume(EBS) of 75GB defined.Tried pip install transformers==4.29.0 bitsandbytes==0.39.1 accelerate==0.20import torch, osfrom transformers import pipelineimport pandas as pdos.environ[“CUDA_VISIBLE_DEVICES”] = “0”device = torch.device(“cuda” if torch.cuda.is_available() else “cpu”)print(device)#cudaLoading in half precision (FP16)generate_text = pipeline(model=“google/flan-ul2”, trust_remote_code=True, torch_dtype=torch.bfloat16, device_map=“auto”, do_sample=False, max_length = 350)Error traceback:OSError                                   Traceback (most recent call last)Cell In[21], line 21 # Half precision (FP16)----> 2 generate_text = pipeline(model=“google/flan-ul2”, trust_remote_code=True, torch_dtype=torch.bfloat16,3                          device_map=“auto”, do_sample=False, max_length = 350)File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/transformers/pipelines/init.py:705, in pipeline(task, model, config, tokenizer, feature_extractor, image_processor, framework, revision, use_fast, use_auth_token, device, device_map, torch_dtype, trust_remote_code, model_kwargs, pipeline_class, **kwargs)703     hub_kwargs[“_commit_hash”] = config._commit_hash704 elif config is None and isinstance(model, str):→ 705     config = AutoConfig.from_pretrained(model, _from_pipeline=task, **hub_kwargs, **model_kwargs)706     hub_kwargs[“_commit_hash”] = config._commit_hash708 custom_tasks = {}File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/transformers/models/auto/configuration_auto.py:928, in AutoConfig.from_pretrained(cls, pretrained_model_name_or_path, **kwargs)926 kwargs[“name_or_path”] = pretrained_model_name_or_path927 trust_remote_code = kwargs.pop(“trust_remote_code”, False)→ 928 config_dict, unused_kwargs = PretrainedConfig.get_config_dict(pretrained_model_name_or_path, **kwargs)929 if “auto_map” in config_dict and “AutoConfig” in config_dict[“auto_map”]:930     if not trust_remote_code:File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/transformers/configuration_utils.py:574, in PretrainedConfig.get_config_dict(cls, pretrained_model_name_or_path, **kwargs)572 original_kwargs = copy.deepcopy(kwargs)573 # Get config dict associated with the base config file→ 574 config_dict, kwargs = cls._get_config_dict(pretrained_model_name_or_path, **kwargs)575 if “_commit_hash” in config_dict:576     original_kwargs[“_commit_hash”] = config_dict[“_commit_hash”]File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/transformers/configuration_utils.py:629, in PretrainedConfig._get_config_dict(cls, pretrained_model_name_or_path, **kwargs)625 configuration_file = kwargs.pop(“_configuration_file”, CONFIG_NAME)627 try:628     # Load from local folder or from cache or download from model Hub and cache→ 629     resolved_config_file = cached_file(630         pretrained_model_name_or_path,631         configuration_file,632         cache_dir=cache_dir,633         force_download=force_download,634         proxies=proxies,635         resume_download=resume_download,636         local_files_only=local_files_only,637         use_auth_token=use_auth_token,638         user_agent=user_agent,639         revision=revision,640         subfolder=subfolder,641         _commit_hash=commit_hash,642     )643     commit_hash = extract_commit_hash(resolved_config_file, commit_hash)644 except EnvironmentError:645     # Raise any environment error raise bycached_file. It will have a helpful error message adapted to646     # the original exception.File ~/anaconda3/envs/pytorch_p310/lib/python3.10/site-packages/transformers/utils/hub.py:388, in cached_file(path_or_repo_id, filename, cache_dir, force_download, resume_download, proxies, use_auth_token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash)386 if not os.path.isfile(resolved_file):387     if _raise_exceptions_for_missing_entries:→ 388         raise EnvironmentError(389             f"{path_or_repo_id} does not appear to have a file named {full_filename}. Checkout "390             f"‘https://huggingface.co/{path_or_repo_id}/{revision}’ for available files."391         )392     else:393         return NoneOSError: google/flan-ul2 does not appear to have a file named config.json. Checkout ‘https://huggingface.co/google/flan-ul2/None’ for available files.
2023-08-07T09:16:02Z
I’m getting the exact same error when trying to deploy thenvidia/stt_en_conformer_transducer_xlargemodel to anInference Endpoint.
2023-08-07T09:25:17Z
Hi@taus-developer, that’s expected given that the repo you link to does not contain a HF Transformers compatible checkpoint. The model seems to be a NeMo model, hence it’s required to create acustom handlerin case you want to create an endpoint for it.
2023-08-07T09:40:30Z
Thanks for the info@nielsr! I somehow missed that connection.
2024-01-25T01:48:08Z
Hello, I’m facing the same problem while running docker on a DPO model. My model is not hosted on hugging face. How did you solve the issue?@taus-developer
2024-01-25T07:23:20Z
nielsrCan I create a custom handler if my model is not hosted on hugging face hub?I’ve a DPO model (based on fine-tuned Mistral 7b) and I need to draw inference from the DPO model.When I dondocker run, it fails to initialize the model with the error message (DPO_output_mistral_32kis the DPO output folder):OSError: /data/DPO_output_mistral_32k does not appear to have a file named config.json. Checkout 'https://huggingface.co//data/DPO_output_mistral_32k/None' for available files.I’m running the training on Amazon linux. Can I create a custom handler on my system to draw inference?
2024-09-17T14:07:15Z
pipe = StableDiffusionControlNetPipeline.from_pretrained(
        args.sd_model,
        vae=vae,
        text_encoder=text_encoder,
        tokenizer=tokenizer,
        unet=unet,
        controlnet=controlnet,
        torch_dtype=weight_dtype,
        safety_checker=None,
        revision=args.revision,
        variant=args.variant,
    )Referring to this example code, usesafety_checker=Nonewould solve the problem.