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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()
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