|
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() |
|
|