I recently ran into this problem, and after reading the above comments resolved it by giving the absolute path to the folder instead of the relative one.

I'm not sure if this is a hugging face issue, or the cluster environment I'm working on, but I thought I would mention it here in case it helps anyone.

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I still get this error:

peftmodelpath = "/notebooks/eva/model.bin"

model = PeftModelForCausalLM.from_pretrained(
    model, 
    peftmodelpath, 
    cache_dir=peftmodelpath, 
    local_files_only=True, 
    model_head_file=None
)

HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/notebooks/eva/model.bin'. Use repo_type argument if needed.

from setfit.

I get the same error. When I try to load a locally saved model:

from setfit import SetFitModel

model = SetFitModel.from_pretrained("/path/to/model-directory", local_files_only=True)

I get

HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/path/to/model-directory'. Use `repo_type` argument if needed.

I think this could be solved by changing these lines from

        if os.path.isdir(model_id) and MODEL_HEAD_NAME in os.listdir(model_id):
            model_head_file = os.path.join(model_id, MODEL_HEAD_NAME)

to something like

        if os.path.isdir(model_id):
            if MODEL_HEAD_NAME in os.listdir(model_id):
                model_head_file = os.path.join(model_id, MODEL_HEAD_NAME)
            else:
                model_head_file = None

from setfit.

I saved a trained model on local path. I can't see anything wrong when loading the model using from_pretrained with correct path.

This error appears when you try to load a model from a nonexistent local path which have more than 1 backslash \ with local_files_only=True.

• if we pass a nonexistent path:
  • if path is in the form 'repo_name' or 'namespace/repo_name': ModelHubMixin.from_pretrained will throw FileNotFoundError.
  • else: ModelHubMixin.from_pretrained will throw a HFValidationError because the path does not exist locally and is not in the expected hub form.
• if we pass an existing local folder with no body: SentenceTransformer will throw an OSError: /path/to/your/model does not appear to have a file named config.json
• if we pass an existing folder with a body and head: no issue in this case
• if we pass an existing folder with a body with no head: SetFitModel._from_pretrained throws HFValidationError not catched by try/except. Since the expected behaviour in SetFit _from_pretrained classmethod is to initialise classification head with random weights when the MODEL_HEAD_NAME is not found, this can be considered as a bug.

@pdhall99 's suggestion looks fixing the issue and follows the same logic in the ModelHubMixin.from_pretrained:

• if folder exists:
  • if model head file exists => model_head_file = os.path.join(model_id, MODEL_HEAD_NAME) else None => initialise classification head with random weights
• else: look for the model head in the hub using hf_hub_download.

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@kryptec thank you so much, I tried your method and gave it the absolute path, and apparently, it works now!

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In the Colab this happens if you mount drive. For some reason mounted path is not recognized. Instead, try having model in /content. This should solve issue of missing path.

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Hello,

I am trying to read the hf model directly from s3 on sagemaker studio. I am also getting same 'HFValidationError' error. I am putting my code below:

`from transformers import T5Tokenizer, T5ForConditionalGeneration

Specify the S3 URL to your model and tokenizer

model_url = "s3://bucketname/model/"

Load the model and tokenizer from S3

tokenizer = T5Tokenizer.from_pretrained(model_url)
model = T5ForConditionalGeneration.from_pretrained(model_url)

Now you can use the model and tokenizer for inference

input_text = "translate English to German: How old are you?"
input_ids = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
input_ids.to("cuda")

outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))`

I am able to see the model by running below code on sagemaker, so i am sure the path is correct.

`s3 = boto3.client('s3')

List all objects in the model folder from S3

s3_resource = boto3.resource('s3')
my_bucket = s3_resource.Bucket(bucket_name)
for object_summary in my_bucket.objects.filter(Prefix=''):
file_path = object_summary.key
file_name = os.path.basename(file_path)
if file_name:
print(file_name)`

Can you please help me? Thanks :)

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Hey @jrivd can you provide a code snippet / Colab that reproduces the error? This will help debug what exactly is going on :)

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Hi @lewtun, thanks for your response. You can see the error in action here: https://colab.research.google.com/drive/10t9QmQEe7BHIQQ8XUmw1B37vPFRfORDK?usp=sharing
Many thanks!

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Many thanks for your comments, @pdhall99 and @Mouhanedg56! I'll go over them carefully

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Hey @pdhall99 , was this issue finally resolved? I am trying to load the ST models offline but still get the repo_name' or 'namespace/repo_name' error when using full path . i can only load models offline in my environment

When the model file is located in same directory still unable to load as below.
model = SetFitModel.from_pretrained("./all-MiniLM-L6-v2",model_head_file=None,local_files_only=True). i tried with multiple arguments but getting the error Repo id must use alphanumeric chars or '-', '_', '.', '--' and '..' are forbidden, '-' and '.' cannot start Thks

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Hi @sfernandes-gim, this change is not yet merged.

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Hi folks, we've just released a new version that include fixes to some of the above issues. For those still having troubles, could you please comment below with a code snippet for debugging? Thanks!

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Hi @lewtun , trying to load from my local directory but always gives below error.Also tried with local_files_only=True option .Please advise

model = SetFitModel.from_pretrained("./all-MiniLM-L6-v2")

Error
HFValidationError: Repo id must use alphanumeric chars or '-', '', '.', '--' and '..' are forbidden, '-' and '.' cannot start or end the name, max length is 96: './all-MiniLM-L6-v2'._

Tried below
odel = SetFitModel.from_pretrained("./Output/all-MiniLM-L6-v2",local_files_only=True,use_differentiable_head=True, head_params={"out_features": num_classes})

Error
HFValidationError: Repo id must be in the form 'repo_name' or 'namespace/repo_name': './Output/all-MiniLM-L6-v2'. Use repo_type argument if needed.

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Thanks @lewtun this works fine with the latest release

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I had the same issue in the local machine (not colab). I was using mounted drive (pure storage).

Creating a symlink to a folder (saving directory) to the working directory (where the training is going on) solved the issue for me.

ln -s existing_source_file optional_symbolic_link

from setfit.

In the Colab this happens if you mount drive. For some reason mounted path is not recognized. Instead, try having model in /content. This should solve issue of missing path.

Not working event loading model from /content. Have you solve this issue from your side ? @bojanbabic

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