I’ve tried deploying the XGLM model on Sagemaker but it wasn’t working. So i tried to load the model as a PreTrainedModel with a PretrainedConfig. However i’m finding that there is no actual support for this text-generative model supported by Hugging Face like the other models. When I try to use AutoConfig it say’s Hugging face doesn’t have a support for this model.
I was able to load the model since it was a ‘.bin’ format which I think is a PyTorch model, however we could also convert it to a TensorflowModel using the TFPretrainedModel, when I load the model using the initial method I get a message where the weights of the model checkpoint were not initialized contain most of the layers.
However when I load with the TFPretrainedModel it loads but asks for the input and output dimensions. My end goal is to utilize this model to build a API around it, can I get any help or support from Hugging Face from this?
from transformers import PreTrainedModel, TFTrainedModel from transformers import PretrainedConfig import json def open_json(path): with open(path, 'r') as f: data = json.load(f) return data def main(): json_config=open_json(base_path+'config.json') config=json.dumps(json_config) cf=PretrainedConfig(name_or_path =config) model =PreTrainedModel.from_pretrained(pretrained_model_name_or_path = './',config=cf) model=TFPretrainedModel.from_pretrained(pretrained_model_name_or_path = './',config=cf, from_pt = True) main()