| import gradio as gr | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| from peft import PeftModel | |
| import torch | |
| def merge(base_model, trained_adapter, token): | |
| base = AutoModelForCausalLM.from_pretrained( | |
| base_model, torch_dtype=torch.float16, low_cpu_mem_usage=True, token=token | |
| ) | |
| model = PeftModel.from_pretrained(base, trained_adapter, token=token) | |
| try: | |
| tokenizer = AutoTokenizer.from_pretrained(base_model, token=token) | |
| except RecursionError: | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| base_model, unk_token="<unk>", token=token | |
| ) | |
| model = model.merge_and_unload() | |
| print("Saving target model") | |
| model.push_to_hub(trained_adapter, token=token) | |
| tokenizer.push_to_hub(trained_adapter, token=token) | |
| return gr.Markdown.update( | |
| value="Model successfully merged and pushed! Please shutdown/pause this space" | |
| ) | |
| with gr.Blocks() as demo: | |
| gr.Markdown("## AutoTrain Merge Adapter") | |
| gr.Markdown("Please duplicate this space and attach a GPU in order to use it.") | |
| token = gr.Textbox( | |
| label="Hugging Face Write Token", | |
| value="", | |
| lines=1, | |
| max_lines=1, | |
| interactive=True, | |
| type="password", | |
| ) | |
| base_model = gr.Textbox( | |
| label="Base Model (e.g. meta-llama/Llama-2-7b-chat-hf)", | |
| value="", | |
| lines=1, | |
| max_lines=1, | |
| interactive=True, | |
| ) | |
| trained_adapter = gr.Textbox( | |
| label="Trained Adapter Model (e.g. username/autotrain-my-llama)", | |
| value="", | |
| lines=1, | |
| max_lines=1, | |
| interactive=True, | |
| ) | |
| submit = gr.Button(value="Merge & Push") | |
| op = gr.Markdown(interactive=False) | |
| submit.click(merge, inputs=[base_model, trained_adapter, token], outputs=[op]) | |
| if __name__ == "__main__": | |
| demo.launch() | |