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initial multi-lora support (#1103)
--------- Co-authored-by: oobabooga <112222186+oobabooga@users.noreply.github.com>
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@ -4,19 +4,31 @@ import torch
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from peft import PeftModel
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from peft import PeftModel
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import modules.shared as shared
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import modules.shared as shared
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from modules.models import reload_model
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def add_lora_to_model(lora_name):
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def add_lora_to_model(lora_names):
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prior_set = set(shared.lora_names)
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added_set = set(lora_names) - prior_set
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removed_set = prior_set - set(lora_names)
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shared.lora_names = list(lora_names)
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# If a LoRA had been previously loaded, or if we want
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# Nothing to do = skip.
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# to unload a LoRA, reload the model
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if len(added_set) == 0 and len(removed_set) == 0:
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if shared.lora_name not in ['None', ''] or lora_name in ['None', '']:
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return
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reload_model()
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shared.lora_name = lora_name
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if lora_name not in ['None', '']:
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# Only adding, and already peft? Do it the easy way.
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print(f"Adding the LoRA {lora_name} to the model...")
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if len(removed_set) == 0 and len(prior_set) > 0:
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print(f"Adding the LoRA(s) named {added_set} to the model...")
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for lora in added_set:
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shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
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return
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# If removing anything, disable all and re-add.
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if len(removed_set) > 0:
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shared.model.disable_adapter()
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if len(lora_names) > 0:
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print("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join(lora_names)))
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params = {}
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params = {}
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if not shared.args.cpu:
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if not shared.args.cpu:
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params['dtype'] = shared.model.dtype
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params['dtype'] = shared.model.dtype
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@ -25,7 +37,11 @@ def add_lora_to_model(lora_name):
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elif shared.args.load_in_8bit:
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elif shared.args.load_in_8bit:
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params['device_map'] = {'': 0}
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params['device_map'] = {'': 0}
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shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_name}"), **params)
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shared.model = PeftModel.from_pretrained(shared.model, Path(f"{shared.args.lora_dir}/{lora_names[0]}"), **params)
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for lora in lora_names[1:]:
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shared.model.load_adapter(Path(f"{shared.args.lora_dir}/{lora}"), lora)
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if not shared.args.load_in_8bit and not shared.args.cpu:
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if not shared.args.load_in_8bit and not shared.args.cpu:
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shared.model.half()
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shared.model.half()
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if not hasattr(shared.model, "hf_device_map"):
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if not hasattr(shared.model, "hf_device_map"):
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@ -6,7 +6,7 @@ import yaml
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model = None
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model = None
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tokenizer = None
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tokenizer = None
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model_name = "None"
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model_name = "None"
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lora_name = "None"
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lora_names = []
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soft_prompt_tensor = None
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soft_prompt_tensor = None
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soft_prompt = False
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soft_prompt = False
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is_RWKV = False
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is_RWKV = False
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@ -68,7 +68,7 @@ settings = {
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},
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},
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'lora_prompts': {
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'lora_prompts': {
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'default': 'QA',
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'default': 'QA',
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'.*(alpaca-lora-7b|alpaca-lora-13b|alpaca-lora-30b)': "Alpaca",
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'.*alpaca': "Alpaca",
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}
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}
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}
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}
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@ -5,13 +5,13 @@ gradio==3.24.1
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markdown
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markdown
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numpy
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numpy
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Pillow>=9.5.0
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Pillow>=9.5.0
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peft==0.2.0
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requests
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requests
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rwkv==0.7.3
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rwkv==0.7.3
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safetensors==0.3.0
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safetensors==0.3.0
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sentencepiece
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sentencepiece
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pyyaml
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pyyaml
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tqdm
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tqdm
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git+https://github.com/huggingface/peft
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transformers==4.28.0
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transformers==4.28.0
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bitsandbytes==0.38.1; platform_system != "Windows"
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bitsandbytes==0.38.1; platform_system != "Windows"
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llama-cpp-python==0.1.33; platform_system != "Windows"
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llama-cpp-python==0.1.33; platform_system != "Windows"
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21
server.py
21
server.py
@ -88,9 +88,10 @@ def load_model_wrapper(selected_model):
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yield traceback.format_exc()
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yield traceback.format_exc()
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def load_lora_wrapper(selected_lora):
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def load_lora_wrapper(selected_loras):
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add_lora_to_model(selected_lora)
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yield ("Applying the following LoRAs to {}:\n\n{}".format(shared.model_name, '\n'.join(selected_loras)))
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return selected_lora
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add_lora_to_model(selected_loras)
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yield ("Successfuly applied the LoRAs")
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def load_preset_values(preset_menu, state, return_dict=False):
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def load_preset_values(preset_menu, state, return_dict=False):
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@ -275,10 +276,12 @@ def create_model_menus():
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with gr.Column():
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with gr.Column():
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with gr.Row():
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with gr.Row():
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shared.gradio['lora_menu'] = gr.Dropdown(choices=get_available_loras(), value=shared.lora_name, label='LoRA')
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shared.gradio['lora_menu'] = gr.Dropdown(multiselect=True, choices=get_available_loras(), value=shared.lora_names, label='LoRA(s)')
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ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': get_available_loras()}, 'refresh-button')
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ui.create_refresh_button(shared.gradio['lora_menu'], lambda: None, lambda: {'choices': get_available_loras(), 'value': shared.lora_names}, 'refresh-button')
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with gr.Column():
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with gr.Column():
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shared.gradio['lora_menu_apply'] = gr.Button(value='Apply the selected LoRAs')
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with gr.Row():
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unload = gr.Button("Unload the model")
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unload = gr.Button("Unload the model")
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reload = gr.Button("Reload the model")
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reload = gr.Button("Reload the model")
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@ -338,7 +341,7 @@ def create_model_menus():
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update_model_parameters, shared.gradio['interface_state'], None).then(
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update_model_parameters, shared.gradio['interface_state'], None).then(
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load_model_wrapper, shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=True)
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load_model_wrapper, shared.gradio['model_menu'], shared.gradio['model_status'], show_progress=True)
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shared.gradio['lora_menu'].change(load_lora_wrapper, shared.gradio['lora_menu'], shared.gradio['lora_menu'], show_progress=True)
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shared.gradio['lora_menu_apply'].click(load_lora_wrapper, shared.gradio['lora_menu'], shared.gradio['model_status'], show_progress=False)
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shared.gradio['download_button'].click(download_model_wrapper, shared.gradio['custom_model_menu'], shared.gradio['model_status'], show_progress=False)
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shared.gradio['download_button'].click(download_model_wrapper, shared.gradio['custom_model_menu'], shared.gradio['model_status'], show_progress=False)
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@ -428,8 +431,8 @@ def create_interface():
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# Defining some variables
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# Defining some variables
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gen_events = []
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gen_events = []
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default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
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default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
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if shared.lora_name != "None":
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if len(shared.lora_names) == 1:
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default_text = load_prompt(shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')])
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default_text = load_prompt(shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_names[0].lower())), 'default')])
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else:
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else:
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default_text = load_prompt(shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')])
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default_text = load_prompt(shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')])
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title = 'Text generation web UI'
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title = 'Text generation web UI'
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@ -861,7 +864,7 @@ if __name__ == "__main__":
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# Load the model
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# Load the model
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shared.model, shared.tokenizer = load_model(shared.model_name)
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shared.model, shared.tokenizer = load_model(shared.model_name)
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if shared.args.lora:
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if shared.args.lora:
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add_lora_to_model(shared.args.lora)
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add_lora_to_model([shared.args.lora])
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# Launch the web UI
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# Launch the web UI
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create_interface()
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create_interface()
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