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import gradio as gr
import os
from huggingface_hub import HfApi, login
from transformers import AutoTokenizer, AutoModelForCausalLM
# NVidia A10G Large (sleep after 1 hour)
# google/gemma-2-9b-it
# meta-llama/Meta-Llama-3-8B-Instruct
profile = "bstraehle"
def download_tokenizer_and_model(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
model.save_pretrained(model_id)
return tokenizer, model
def upload_model(tokenizer, model)
model_name = model_id[model_id.rfind('/')+1:]
print(model_name)
model_repo_name = f"{profile}/{model_name}"
login(token=os.environ["HF_TOKEN"])
api = HfApi()
api.create_repo(repo_id=model_repo_name)
api.upload_folder(
folder_path=model_path,
repo_id=model_repo_name
)
tokenizer.push_to_hub(model_repo_name)
def process(model_id, dataset):
tokenizer, model = download_model(model_id)
upload_model(tokenizer, model)
return "Done"
demo = gr.Interface(fn=process,
inputs=[gr.Textbox(label = "Model ID", value = "meta-llama/Meta-Llama-3-8B-Instruct", lines = 1),
gr.Textbox(label = "Dataset", value = "imdb", lines = 1)],
outputs=[gr.Textbox(label = "Completion")])
demo.launch() |