File size: 1,843 Bytes
c445497 |
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 55 56 57 58 59 60 61 |
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()
|