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