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| from __future__ import annotations | |
| from typing import Iterable | |
| import gradio as gr | |
| from gradio.themes.base import Base | |
| from gradio.themes.utils import colors, fonts, sizes | |
| from llama_cpp import Llama | |
| from huggingface_hub import hf_hub_download | |
| hf_hub_download(repo_id="Monster/a", filename="ggml-alpaca-7b-q4.bin", local_dir=".") | |
| llm = Llama(model_path="./ggml-alpaca-7b-q4.bin") | |
| ins = '''Below is an instruction that describes a task. Write a response that appropriately completes the request. | |
| ### Instruction: | |
| {} | |
| ### Response: | |
| ''' | |
| ins_inp = '''Below is an instruction that describes a task. Write a response that appropriately completes the request. | |
| ### Instruction: | |
| {} | |
| ### Input: | |
| {} | |
| ### Response: | |
| ''' | |
| theme = gr.themes.Monochrome( | |
| primary_hue="indigo", | |
| secondary_hue="blue", | |
| neutral_hue="slate", | |
| radius_size=gr.themes.sizes.radius_sm, | |
| font=[gr.themes.GoogleFont("Open Sans"), "ui-sans-serif", "system-ui", "sans-serif"], | |
| ) | |
| def generate( | |
| instruction, | |
| input=None, | |
| temperature=0.1, | |
| top_p=0.95, | |
| top_k=40,): | |
| result = "" | |
| if input: | |
| instruction = ins_inp.format(instruction, input) | |
| else: | |
| instruction = ins.format(instruction) | |
| for x in llm(instruction, stop=['### Instruction:', '### End'], stream=True, temperature=temperature, top_p=top_p, top_k=top_k): | |
| result += x['choices'][0]['text'] | |
| yield result | |
| examples = [ | |
| "Instead of making a peanut butter and jelly sandwich, what else could I combine peanut butter with in a sandwich? Give five ideas", | |
| "How do I make a campfire?", | |
| "Explain to me the difference between nuclear fission and fusion.", | |
| "Write an ad for sale Nikon D750." | |
| ] | |
| def process_example(args): | |
| for x in generate(args): | |
| pass | |
| return x | |
| css = ".generating {visibility: hidden}" | |
| # Based on the gradio theming guide and borrowed from https://huggingface.co/spaces/shivi/dolly-v2-demo | |
| class SeafoamCustom(Base): | |
| def __init__( | |
| self, | |
| *, | |
| primary_hue: colors.Color | str = colors.emerald, | |
| secondary_hue: colors.Color | str = colors.blue, | |
| neutral_hue: colors.Color | str = colors.blue, | |
| spacing_size: sizes.Size | str = sizes.spacing_md, | |
| radius_size: sizes.Size | str = sizes.radius_md, | |
| font: fonts.Font | |
| | str | |
| | Iterable[fonts.Font | str] = ( | |
| fonts.GoogleFont("Quicksand"), | |
| "ui-sans-serif", | |
| "sans-serif", | |
| ), | |
| font_mono: fonts.Font | |
| | str | |
| | Iterable[fonts.Font | str] = ( | |
| fonts.GoogleFont("IBM Plex Mono"), | |
| "ui-monospace", | |
| "monospace", | |
| ), | |
| ): | |
| super().__init__( | |
| primary_hue=primary_hue, | |
| secondary_hue=secondary_hue, | |
| neutral_hue=neutral_hue, | |
| spacing_size=spacing_size, | |
| radius_size=radius_size, | |
| font=font, | |
| font_mono=font_mono, | |
| ) | |
| super().set( | |
| button_primary_background_fill="linear-gradient(90deg, *primary_300, *secondary_400)", | |
| button_primary_background_fill_hover="linear-gradient(90deg, *primary_200, *secondary_300)", | |
| button_primary_text_color="white", | |
| button_primary_background_fill_dark="linear-gradient(90deg, *primary_600, *secondary_800)", | |
| block_shadow="*shadow_drop_lg", | |
| button_shadow="*shadow_drop_lg", | |
| input_background_fill="zinc", | |
| input_border_color="*secondary_300", | |
| input_shadow="*shadow_drop", | |
| input_shadow_focus="*shadow_drop_lg", | |
| ) | |
| seafoam = SeafoamCustom() | |
| with gr.Blocks(theme=seafoam, analytics_enabled=False, css=css) as demo: | |
| with gr.Column(): | |
| gr.Markdown( | |
| """ ## Alpaca-LoRa | |
| 7b quantized 4bit (q4_0) | |
| Type in the box below and click the button to generate answers to your most pressing questions! | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| instruction = gr.Textbox(lines=2, placeholder="Tell me more about alpacas.", label="Instruction", elem_id="q-input") | |
| with gr.Accordion("Advanced setting", open=False): | |
| input = gr.components.Textbox(lines=2, label="Input", placeholder="none") | |
| temperature = gr.components.Slider(minimum=0, maximum=1, value=0.1, label="Temperature") | |
| top_p = gr.components.Slider(minimum=0, maximum=1, value=0.95, label="Top p") | |
| top_k = gr.components.Slider(minimum=0, maximum=100, step=1, value=40, label="Top k") | |
| with gr.Box(): | |
| gr.Markdown("**Output**") | |
| output = gr.Markdown(elem_id="q-output") | |
| submit = gr.Button("Generate", variant="primary") | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[instruction], | |
| cache_examples=False, | |
| fn=process_example, | |
| outputs=[output], | |
| ) | |
| submit.click(generate, inputs=[instruction, input, temperature, top_p, top_k], outputs=[output]) | |
| instruction.submit(generate, inputs=[instruction], outputs=[output]) | |
| demo.queue(concurrency_count=1).launch(debug=True) |