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Add chat mode
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@ -12,6 +12,7 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.
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* Generate nice HTML output for gpt4chan.
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* Generate Markdown output for [GALACTICA](https://github.com/paperswithcode/galai), including LaTeX support.
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* Notebook mode that resembles OpenAI's playground.
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* Chat mode for conversation and role playing.
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* Load 13b/20b models in 8-bit mode.
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* Load parameter presets from text files.
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@ -84,6 +85,8 @@ Optionally, you can use the following command-line flags:
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`--notebook`: Launch the webui in notebook mode, where the output is written to the same text box as the input.
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`--chat`: Launch the webui in chat mode.
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## Presets
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Inference settings presets can be created under `presets/` as text files. These files are detected automatically at startup.
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50
server.py
50
server.py
@ -15,6 +15,7 @@ from transformers import AutoModelForCausalLM, T5ForConditionalGeneration
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parser = argparse.ArgumentParser()
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parser.add_argument('--model', type=str, help='Name of the model to load by default.')
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parser.add_argument('--notebook', action='store_true', help='Launch the webui in notebook mode, where the output is written to the same text box as the input.')
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parser.add_argument('--chat', action='store_true', help='Launch the webui in chat mode.')
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args = parser.parse_args()
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loaded_preset = None
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available_models = sorted(set(map(lambda x : str(x.name).replace('.pt', ''), list(Path('models/').glob('*'))+list(Path('torch-dumps/').glob('*')))))
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@ -148,6 +149,55 @@ if args.notebook:
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model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
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btn.click(generate_reply, [textbox, temp_slider, length_slider, preset_menu, model_menu], [textbox, markdown, html], show_progress=False)
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elif args.chat:
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history = []
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def chatbot(text, temperature, max_length, inference_settings, selected_model, name1, name2, context):
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question = context+'\n\n'
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for i in range(len(history)):
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question += f"{name1}: {history[i][0][3:-5].strip()}\n"
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question += f"{name2}: {history[i][1][3:-5].strip()}\n"
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question += f"{name1}: {text.strip()}\n"
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question += f"{name2}:"
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reply = generate_reply(question, temperature, max_length, inference_settings, selected_model)[0]
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reply = reply[len(question):].split('\n')[0].strip()
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history.append((text, reply))
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return history
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def clear():
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global history
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history = []
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with gr.Blocks(css=".my-4 {margin-top: 0} .py-6 {padding-top: 2.5rem}") as interface:
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gr.Markdown(
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f"""
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# Text generation lab
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Generate text using Large Language Models.
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"""
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)
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with gr.Row(equal_height=True):
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with gr.Column():
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with gr.Row(equal_height=True):
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with gr.Column():
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length_slider = gr.Slider(minimum=1, maximum=2000, step=1, label='max_length', value=200)
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preset_menu = gr.Dropdown(choices=available_presets, value="NovelAI-Sphinx Moth", label='Preset')
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with gr.Column():
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temp_slider = gr.Slider(minimum=0.0, maximum=1.0, step=0.01, label='Temperature', value=0.7)
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model_menu = gr.Dropdown(choices=available_models, value=model_name, label='Model')
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name1 = gr.Textbox(value='Person 1', lines=1, label='Your name')
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name2 = gr.Textbox(value='Person 2', lines=1, label='Bot\'s name')
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context = gr.Textbox(value='This is a conversation between two people.', lines=2, label='Context')
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with gr.Column():
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display1 = gr.Chatbot()
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textbox = gr.Textbox(lines=2)
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btn = gr.Button("Generate")
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btn2 = gr.Button("Clear history")
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btn.click(chatbot, [textbox, temp_slider, length_slider, preset_menu, model_menu, name1, name2, context], display1, show_progress=True)
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btn2.click(clear)
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else:
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with gr.Blocks(css=".my-4 {margin-top: 0} .py-6 {padding-top: 2.5rem}") as interface:
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gr.Markdown(
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