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https://github.com/oobabooga/text-generation-webui.git
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Add "Save current settings for this model" button
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.gitignore
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@ -24,3 +24,4 @@ settings.json
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img_bot*
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img_me*
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prompts/[0-9]*
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models/config-user.yaml
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@ -45,17 +45,14 @@ def load_model(model_name):
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shared.is_RWKV = 'rwkv-' in model_name.lower()
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shared.is_llamacpp = len(list(Path(f'{shared.args.model_dir}/{model_name}').glob('ggml*.bin'))) > 0
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# Default settings
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# Load the model in simple 16-bit mode by default
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if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.wbits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV, shared.is_llamacpp]):
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if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
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model = AutoModelForCausalLM.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}"), device_map='auto', load_in_8bit=True)
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model = AutoModelForCausalLM.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
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if torch.has_mps:
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device = torch.device('mps')
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model = model.to(device)
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else:
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model = AutoModelForCausalLM.from_pretrained(Path(f"{shared.args.model_dir}/{shared.model_name}"), low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if shared.args.bf16 else torch.float16)
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if torch.has_mps:
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device = torch.device('mps')
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model = model.to(device)
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else:
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model = model.cuda()
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model = model.cuda()
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# FlexGen
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elif shared.args.flexgen:
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36
server.py
36
server.py
@ -21,6 +21,7 @@ from pathlib import Path
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import gradio as gr
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import psutil
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import torch
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import yaml
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from PIL import Image
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import modules.extensions as extensions_module
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@ -233,7 +234,7 @@ def get_model_specific_settings(model):
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model_settings = {}
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for pat in settings:
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if re.match(pat, model.lower()):
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if re.match(pat.lower(), model.lower()):
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for k in settings[pat]:
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model_settings[k] = settings[pat][k]
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@ -249,6 +250,29 @@ def load_model_specific_settings(model, state, return_dict=False):
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return state
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def save_model_settings(model, state):
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if model == 'None':
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yield ("Not saving the settings because no model is loaded.")
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return
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with Path(f'{shared.args.model_dir}/config-user.yaml') as p:
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if p.exists():
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user_config = yaml.safe_load(open(p, 'r').read())
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else:
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user_config = {}
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if model not in user_config:
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user_config[model] = {}
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for k in ui.list_model_elements():
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user_config[model][k] = state[k]
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with open(p, 'w') as f:
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f.write(yaml.dump(user_config))
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yield (f"Settings for {model} saved to {p}")
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def create_model_menus():
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# Finding the default values for the GPU and CPU memories
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total_mem = []
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@ -285,10 +309,12 @@ def create_model_menus():
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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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shared.gradio['lora_menu_apply'] = gr.Button(value='Apply the selected LoRAs')
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with gr.Row():
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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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reload = gr.Button("Reload the model")
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save_settings = gr.Button("Save current settings for this model")
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with gr.Row():
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with gr.Column():
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@ -344,7 +370,11 @@ def create_model_menus():
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unload_model, None, None).then(
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ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).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=False)
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save_settings.click(
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ui.gather_interface_values, [shared.gradio[k] for k in shared.input_elements], shared.gradio['interface_state']).then(
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save_model_settings, [shared.gradio[k] for k in ['model_menu', 'interface_state']], shared.gradio['model_status'], show_progress=False)
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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_model_button'].click(download_model_wrapper, shared.gradio['custom_model_menu'], shared.gradio['model_status'], show_progress=False)
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