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| import random | |
| import gradio as gr | |
| import torch | |
| from gradio.themes.utils import sizes | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| import utils | |
| from constants import END_OF_TEXT | |
| # Load the tokenizer and model | |
| tokenizer = AutoTokenizer.from_pretrained( | |
| "BEE-spoke-data/smol_llama-101M-GQA-python", | |
| use_fast=False, | |
| ) | |
| tokenizer.pad_token_id = tokenizer.eos_token_id | |
| tokenizer.pad_token = END_OF_TEXT | |
| model = AutoModelForCausalLM.from_pretrained( | |
| "BEE-spoke-data/smol_llama-101M-GQA-python", | |
| device_map="auto", | |
| ) | |
| model = torch.compile(model, mode="reduce-overhead") | |
| # UI things | |
| _styles = utils.get_file_as_string("styles.css") | |
| # Loads ./README.md file & splits it into sections | |
| readme_file_content = utils.get_file_as_string("README.md", path="./") | |
| ( | |
| manifest, | |
| description, | |
| disclaimer, | |
| base_model_info, | |
| formats, | |
| ) = utils.get_sections(readme_file_content, "---", up_to=5) | |
| theme = gr.themes.Soft( | |
| primary_hue="yellow", | |
| secondary_hue="orange", | |
| neutral_hue="slate", | |
| radius_size=sizes.radius_sm, | |
| font=[ | |
| gr.themes.GoogleFont("IBM Plex Sans", [400, 600]), | |
| "ui-sans-serif", | |
| "system-ui", | |
| "sans-serif", | |
| ], | |
| text_size=sizes.text_lg, | |
| ) | |
| def run_inference( | |
| prompt, temperature, max_new_tokens, top_p, repetition_penalty | |
| ) -> str: | |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
| outputs = model.generate( | |
| **inputs, | |
| do_sample=True, | |
| early_stopping=True, | |
| max_new_tokens=max_new_tokens, | |
| min_new_tokens=8, | |
| no_repeat_ngram_size=6, | |
| num_beams=3, | |
| renormalize_logits=True, | |
| repetition_penalty=repetition_penalty, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ) | |
| text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0] | |
| return text | |
| examples = [ | |
| ["def add_numbers(a, b):\n return", 0.2, 96, 0.9, 1.2], | |
| [ | |
| "class Car:\n def __init__(self, make, model):\n self.make = make\n self.model = model\n\n def display_car(self):", | |
| 0.2, | |
| 96, | |
| 0.9, | |
| 1.2, | |
| ], | |
| [ | |
| "import pandas as pd\ndata = {'Name': ['Tom', 'Nick', 'John'], 'Age': [20, 21, 19]}\ndf = pd.DataFrame(data).convert_dtypes()\n# eda", | |
| 0.2, | |
| 96, | |
| 0.9, | |
| 1.2, | |
| ], | |
| [ | |
| "def factorial(n):\n if n == 0:\n return 1\n else:", | |
| 0.2, | |
| 96, | |
| 0.9, | |
| 1.2, | |
| ], | |
| [ | |
| 'def fibonacci(n):\n if n <= 0:\n raise ValueError("Incorrect input")\n elif n == 1:\n return 0\n elif n == 2:\n return 1\n else:', | |
| 0.2, | |
| 96, | |
| 0.9, | |
| 1.2, | |
| ], | |
| [ | |
| "import matplotlib.pyplot as plt\nimport numpy as np\nx = np.linspace(0, 10, 100)\n# simple plot", | |
| 0.2, | |
| 96, | |
| 0.9, | |
| 1.2, | |
| ], | |
| ["def reverse_string(s:str) -> str:\n return", 0.2, 96, 0.9, 1.2], | |
| ["def is_palindrome(word:str) -> bool:\n return", 0.2, 96, 0.9, 1.2], | |
| [ | |
| "def bubble_sort(lst: list):\n n = len(lst)\n for i in range(n):\n for j in range(0, n-i-1):", | |
| 0.2, | |
| 96, | |
| 0.9, | |
| 1.2, | |
| ], | |
| [ | |
| "def binary_search(arr, low, high, x):\n if high >= low:\n mid = (high + low) // 2\n if arr[mid] == x:\n return mid\n elif arr[mid] > x:", | |
| 0.2, | |
| 96, | |
| 0.9, | |
| 1.2, | |
| ], | |
| ] | |
| # Define the Gradio Blocks interface | |
| with gr.Blocks(theme=theme, analytics_enabled=False, css=_styles) as demo: | |
| with gr.Column(): | |
| gr.Markdown(description) | |
| with gr.Row(): | |
| with gr.Column(): | |
| instruction = gr.Textbox( | |
| value=random.choice([e[0] for e in examples]), | |
| placeholder="Enter your code here", | |
| label="Code", | |
| elem_id="q-input", | |
| ) | |
| submit = gr.Button("Generate", variant="primary") | |
| output = gr.Code(elem_id="q-output", language="python", lines=10) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Accordion("Advanced settings", open=False): | |
| with gr.Row(): | |
| column_1, column_2 = gr.Column(), gr.Column() | |
| with column_1: | |
| temperature = gr.Slider( | |
| label="Temperature", | |
| value=0.2, | |
| minimum=0.0, | |
| maximum=1.0, | |
| step=0.05, | |
| interactive=True, | |
| info="Higher values produce more diverse outputs", | |
| ) | |
| max_new_tokens = gr.Slider( | |
| label="Max new tokens", | |
| value=64, | |
| minimum=32, | |
| maximum=512, | |
| step=32, | |
| interactive=True, | |
| info="Number of tokens to generate", | |
| ) | |
| with column_2: | |
| top_p = gr.Slider( | |
| label="Top-p (nucleus sampling)", | |
| value=0.90, | |
| minimum=0.0, | |
| maximum=1, | |
| step=0.05, | |
| interactive=True, | |
| info="Higher values sample more low-probability tokens", | |
| ) | |
| repetition_penalty = gr.Slider( | |
| label="Repetition penalty", | |
| value=1.1, | |
| minimum=1.0, | |
| maximum=2.0, | |
| step=0.05, | |
| interactive=True, | |
| info="Penalize repeated tokens", | |
| ) | |
| with gr.Column(): | |
| version = gr.Dropdown( | |
| [ | |
| "smol_llama-101M-GQA-python", | |
| ], | |
| value="smol_llama-101M-GQA-python", | |
| label="Version", | |
| info="", | |
| ) | |
| gr.Markdown(disclaimer) | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[ | |
| instruction, | |
| temperature, | |
| max_new_tokens, | |
| top_p, | |
| repetition_penalty, | |
| version, | |
| ], | |
| cache_examples=False, | |
| fn=run_inference, | |
| outputs=[output], | |
| ) | |
| gr.Markdown(base_model_info) | |
| gr.Markdown(formats) | |
| submit.click( | |
| run_inference, | |
| inputs=[ | |
| instruction, | |
| temperature, | |
| max_new_tokens, | |
| top_p, | |
| repetition_penalty, | |
| ], | |
| outputs=[output], | |
| # preprocess=False, | |
| # batch=False, | |
| show_progress=True, | |
| ) | |
| # .queue(max_size=10, api_open=False) | |
| demo.launch( | |
| debug=True, | |
| show_api=False, | |
| share=utils.is_google_colab(), | |
| ) | |