File size: 1,402 Bytes
ac66ae2 cbbb9fd 2acdb22 083fde1 a4754dd c8534fb 251d88f c8534fb 251d88f c8534fb 92146e5 cbbb9fd 2acdb22 c8534fb 251d88f e66c7c3 92146e5 c8534fb 251d88f cbbb9fd 251d88f cbbb9fd 251d88f cbbb9fd 0fb434b 251d88f c8534fb 251d88f 2371111 92146e5 083fde1 2371111 6f58fe9 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 41 42 43 44 45 46 47 48 49 50 51 52 53 54 |
import gradio as gr
import os
from huggingface_hub import HfApi, login
from transformers import AutoTokenizer, AutoModelForCausalLM
# NVidia A10G Large (sleep after 1 hour)
# Model IDs:
# google/gemma-2-9b-it
# meta-llama/Meta-Llama-3-8B-Instruct
# Datasets:
#
#
profile = "bstraehle"
def download_model(model_id):
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
model.save_pretrained(model_id)
return tokenizer
def fine_tune_model():
return ""
def upload_model(model_id, tokenizer):
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_id,
repo_id=model_repo_name
)
tokenizer.push_to_hub(model_repo_name)
def process(model_id, dataset):
tokenizer = download_model(model_id)
upload_model(model_id, tokenizer)
return "Processing completed"
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() |