huggingface

#@title ## 8.1. Upload Config
from huggingface_hub import login
from huggingface_hub import HfApi
from huggingface_hub.utils import validate_repo_id, HfHubHTTPError

#@markdown Login to Huggingface Hub 
#@markdown > Get **your** huggingface `WRITE` token [here](https://huggingface.co/settings/tokens)
write_token = "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" #@param {type:"string"}
login(write_token, add_to_git_credential=True)

api = HfApi()
user = api.whoami(write_token)

#@markdown Fill this if you want to upload to your organization, or just leave it empty.

orgs_name = "" #@param{type:"string"}

#@markdown If your model/dataset repo didn't exist, it will automatically create your repo.
model_name = "cczhong/chinese-alpaca-plus-lora-7b-merged-ggml-4b" #@param{type:"string"}
#dataset_name = "your-dataset-name" #@param{type:"string"}
make_this_model_private = False #@param{type:"boolean"}
model_repo=model_name

if model_name:
  try:
      validate_repo_id(model_repo)
      api.create_repo(repo_id=model_repo, 
                      private=make_this_model_private)
      print("Model Repo didn't exists, creating repo")
      print("Model Repo: ",model_repo,"created!\n")

  except HfHubHTTPError as e:
      print(f"Model Repo: {model_repo} exists, skipping create repo\n")


from huggingface_hub import HfApi
from pathlib import Path
%store -r

api = HfApi()
commit_message = "add new model" #@param {type :"string"}
model_path = "/home/ubuntu/llama.cpp/zh-models/alpaca-combined"
if not commit_message:
  commit_message = "feat: upload "+project_name+" lora model"
path_in_repo = None
def upload_model(model_paths, is_folder :bool, is_config :bool):
  path_obj = Path(model_paths)
  trained_model = path_obj.parts[-1]
  
  if path_in_repo:
    trained_model = path_in_repo
  
  if is_config:
    trained_model = f"{project_name}_config"

  if is_folder:
    print(f"Uploading {trained_model} to https://huggingface.co/"+model_repo)
    print(f"Please wait...")
    
    api.upload_folder(
        folder_path=model_paths,
        path_in_repo=trained_model,
        repo_id=model_repo,
        commit_message=commit_message,
        ignore_patterns=".ipynb_checkpoints"
        )
    print(f"Upload success, located at https://huggingface.co/"+model_repo+"/tree/main\n")
  else: 
    print(f"Uploading {trained_model} to https://huggingface.co/"+model_repo)
    print(f"Please wait...")
            
    api.upload_file(
        path_or_fileobj=model_paths,
        path_in_repo=trained_model,
        repo_id=model_repo,
        commit_message=commit_message,
        )
        
    print(f"Upload success, located at https://huggingface.co/"+model_repo+"/blob/main/"+trained_model+"\n")
      
def upload():
    if model_path.endswith((".ckpt", ".safetensors", ".pt")):
      upload_model(model_path, False, False)
    else:
      upload_model(model_path, True, False)

    #if config_path:
    #  upload_model(config_path, True, True)

upload()