File size: 1,276 Bytes
ac66ae2 cbbb9fd 2acdb22 083fde1 2371111 e51ebe6 cbbb9fd 2acdb22 cbbb9fd e51ebe6 ac66ae2 cbbb9fd 96a61fa cbbb9fd e51ebe6 cbbb9fd e51ebe6 cbbb9fd 0fb434b 2371111 cbbb9fd e51ebe6 cbbb9fd b937d88 083fde1 2371111 96a61fa d91c99b 2371111 083fde1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 |
import gradio as gr
import os
from huggingface_hub import HfApi, login
from transformers import AutoTokenizer, AutoModelForCausalLM
def process(model_id, dataset):
print("111")
# Download Sample Model from Hugging Face to Publish Again
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
# Local Path of Model
print("222")
model_path = model_id
model.save_pretrained(model_path)
login(token=os.environ["HF_TOKEN"])
api = HfApi()
model_repo_name = "bstraehle/gemma-2-9b-it"
#Create Repo in Hugging Face
print("333")
api.create_repo(repo_id=model_repo_name)
#Upload Model folder from Local to HuggingFace
print("444")
api.upload_folder(
folder_path=model_path,
repo_id=model_repo_name
)
# Publish Model Tokenizer on Hugging Face
print("555")
tokenizer.push_to_hub(model_repo_name)
return "Done"
demo = gr.Interface(fn=process,
inputs=[gr.Textbox(label = "Model ID", value = "google/gemma-2-9b-it", lines = 1),
gr.Textbox(label = "Dataset", value = "imdb", lines = 1)],
outputs=[gr.Textbox(label = "Completion")])
demo.launch() |