diff --git a/community-contributions/abdoul/week_three_exercise.ipynb b/community-contributions/abdoul/week_three_exercise.ipynb new file mode 100644 index 0000000..6157e68 --- /dev/null +++ b/community-contributions/abdoul/week_three_exercise.ipynb @@ -0,0 +1,1767 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 22, + "id": "3f7540d9", + "metadata": { + "id": "3f7540d9" + }, + "outputs": [], + "source": [ + "import os\n", + "import requests\n", + "from IPython.display import Markdown, display, update_display\n", + "from openai import OpenAI\n", + "from google.colab import drive\n", + "from huggingface_hub import login\n", + "from google.colab import userdata\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, BitsAndBytesConfig\n", + "import torch\n", + "\n", + "from functools import lru_cache\n", + "from diffusers import StableDiffusionPipeline\n", + "import gradio as gr" + ] + }, + { + "cell_type": "code", + "source": [ + "hf_token = userdata.get('HF_TOKEN')\n", + "login(hf_token, add_to_git_credential=True)" + ], + "metadata": { + "id": "BX0nP9tyGG6S" + }, + "id": "BX0nP9tyGG6S", + "execution_count": 23, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "27c5d024", + "metadata": { + "id": "27c5d024" + }, + "outputs": [], + "source": [ + "TEXT_MODEL_ID = \"meta-llama/Llama-3.2-3B-Instruct\"\n", + "IMAGE_MODEL_ID = \"runwayml/stable-diffusion-v1-5\"\n", + "\n", + "FORMAT_RULES = {\n", + " \"JSON\": \"Return a JSON array containing records_count objects with consistent fields tailored to the context.\",\n", + " \"CSV\": \"Return a CSV document with a header row and records_count data rows aligned to the context.\",\n", + " \"Raw Text\": \"Return records_count prose entries separated by blank lines that reflect the context.\",\n", + " \"Code\": \"Return records_count code snippets grouped in a single fenced block that models the context.\"\n", + "}\n", + "\n", + "@lru_cache(maxsize=2)\n", + "def load_text_components(use_quant: bool):\n", + " tokenizer = AutoTokenizer.from_pretrained(TEXT_MODEL_ID)\n", + " if tokenizer.pad_token is None:\n", + " tokenizer.pad_token = tokenizer.eos_token\n", + " if use_quant:\n", + " quant_config = BitsAndBytesConfig(\n", + " load_in_4bit=True,\n", + " bnb_4bit_use_double_quant=True,\n", + " bnb_4bit_compute_dtype=torch.bfloat16,\n", + " bnb_4bit_quant_type=\"nf4\"\n", + " )\n", + " model = AutoModelForCausalLM.from_pretrained(\n", + " TEXT_MODEL_ID,\n", + " device_map=\"auto\",\n", + " quantization_config=quant_config,\n", + " trust_remote_code=True\n", + " )\n", + " else:\n", + " kwargs = {\"trust_remote_code\": True}\n", + " kwargs[\"device_map\"] = \"auto\"\n", + " kwargs[\"torch_dtype\"] = torch.float16\n", + "\n", + " model = AutoModelForCausalLM.from_pretrained(TEXT_MODEL_ID, **kwargs)\n", + "\n", + " model.eval()\n", + " return tokenizer, model\n", + "\n", + "def build_text_messages(style: str, context: str, return_format: str, record_count: int):\n", + " context_value = context.strip() if context else \"general purpose scenario\"\n", + " style_value = style.strip() if style else \"Balanced\"\n", + " directive = FORMAT_RULES[return_format]\n", + " system_prompt = \"You generate synthetic datasets that are high quality, diverse, and free of personally identifiable information. \" + directive + \" Ensure outputs are consistent in structure, imaginative in content, and avoid explanations.\"\n", + " user_prompt = f\"Context: {context_value}\\nStyle: {style_value}\\nRecords: {record_count}\\nOutput style: {return_format}\"\n", + " return [\n", + " {\"role\": \"system\", \"content\": system_prompt},\n", + " {\"role\": \"user\", \"content\": user_prompt}\n", + " ]\n", + "\n", + "def generate_text_data(style: str, context: str, return_format: str, quantize: bool, record_count: int):\n", + " tokenizer, model = load_text_components(bool(quantize))\n", + " messages = build_text_messages(style, context, return_format, int(record_count))\n", + " inputs = tokenizer.apply_chat_template(messages, return_tensors=\"pt\", add_generation_prompt=True)\n", + " inputs = inputs.to(\"cuda\")\n", + " attention_mask = torch.ones_like(inputs)\n", + " with torch.inference_mode():\n", + " generated = model.generate(\n", + " input_ids=inputs,\n", + " attention_mask=attention_mask,\n", + " max_new_tokens=512,\n", + " temperature=0.7,\n", + " top_p=0.9,\n", + " repetition_penalty=1.05,\n", + " do_sample=True\n", + " )\n", + "\n", + " output_ids = generated[:, inputs.shape[-1]:]\n", + " text = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]\n", + " return text.strip()\n", + "\n", + "@lru_cache(maxsize=1)\n", + "def load_image_pipeline():\n", + " pipeline = StableDiffusionPipeline.from_pretrained(IMAGE_MODEL_ID, torch_dtype=torch.float16)\n", + " pipeline = pipeline.to(\"cuda\")\n", + " return pipeline\n", + "\n", + "def generate_image_data(style: str, context: str, image_prompt: str, image_count: int):\n", + " pipeline = load_image_pipeline()\n", + " parts = []\n", + " if image_prompt:\n", + " parts.append(image_prompt.strip())\n", + "\n", + " if context:\n", + " parts.append(context.strip())\n", + "\n", + " base = \", \".join([p for p in parts if p])\n", + " if not base:\n", + " base = \"Synthetic data concept visualization\"\n", + "\n", + " prompt = f\"{base}, {style.lower()} style\"\n", + " images = pipeline(prompt, num_images_per_prompt=int(image_count), guidance_scale=7.0, num_inference_steps=30).images\n", + " return images\n", + "\n", + "def run_generation(data_type: str, style: str, context: str, return_format: str, quantize: bool, image_prompt: str, record_count: int, image_count: int):\n", + " if data_type == \"Text\":\n", + " text = generate_text_data(style, context, return_format, quantize, record_count)\n", + " return gr.update(value=text, visible=True), gr.update(value=[], visible=False)\n", + "\n", + " images = generate_image_data(style, context, image_prompt, image_count)\n", + " return gr.update(value=\"\", visible=False), gr.update(value=images, visible=True)\n", + "\n", + "def toggle_inputs(data_type: str):\n", + " if data_type == \"Text\":\n", + " return (\n", + " gr.update(visible=True),\n", + " gr.update(visible=True),\n", + " gr.update(visible=False),\n", + " gr.update(visible=True),\n", + " gr.update(visible=False),\n", + " gr.update(value=\"\", visible=True),\n", + " gr.update(value=[], visible=False)\n", + " )\n", + " return (\n", + " gr.update(visible=False),\n", + " gr.update(visible=False),\n", + " gr.update(visible=True),\n", + " gr.update(visible=False),\n", + " gr.update(visible=True),\n", + " gr.update(value=\"\", visible=False),\n", + " gr.update(value=[], visible=True)\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "3d1c45e6", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 919, + "referenced_widgets": [ + 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Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n", + "\n", + "Colab notebook detected. This cell will run indefinitely so that you can see errors and logs. To turn off, set debug=False in launch().\n", + "* Running on public URL: https://b5fd391afd63f4968c.gradio.live\n", + "\n", + "This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
" + ] + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/2 [00:00 https://b5fd391afd63f4968c.gradio.live\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [] + }, + "metadata": {}, + "execution_count": 25 + } + ], + "source": [ + "with gr.Blocks(title=\"Synthetic Data Generator\") as demo:\n", + " gr.Markdown(\"## Synthetic Data Generator\")\n", + " with gr.Row():\n", + " data_type = gr.Radio([\"Text\", \"Image\"], label=\"Type\", value=\"Text\")\n", + " style = gr.Dropdown([\"Concise\", \"Detailed\", \"Narrative\", \"Technical\", \"Tabular\"], label=\"Style\", value=\"Detailed\")\n", + "\n", + " context_input = gr.Textbox(label=\"Context\", lines=4, placeholder=\"Describe the entities, attributes, and purpose of the dataset.\")\n", + " return_format = gr.Dropdown([\"JSON\", \"CSV\", \"Raw Text\", \"Code\"], label=\"Return Format\", value=\"JSON\")\n", + " quantize = gr.Checkbox(label=\"Quantize\", value=False)\n", + " record_count = gr.Slider(1, 20, value=5, step=1, label=\"Records\")\n", + " image_prompt = gr.Textbox(label=\"Image Prompt\", lines=2, visible=False, placeholder=\"Detail the visual you want to synthesize.\")\n", + " image_count = gr.Slider(1, 4, value=1, step=1, label=\"Images\", visible=False)\n", + " generate_button = gr.Button(\"Generate\")\n", + " text_output = gr.Textbox(label=\"Text Output\", lines=12)\n", + " image_output = gr.Gallery(label=\"Generated Images\", visible=False, columns=2, rows=1)\n", + " data_type.change(\n", + " toggle_inputs,\n", + " inputs=data_type,\n", + " outputs=[return_format, quantize, image_prompt, record_count, image_count, text_output, image_output]\n", + " )\n", + " generate_button.click(\n", + " run_generation,\n", + " inputs=[data_type, style, context_input, return_format, quantize, image_prompt, record_count, image_count],\n", + " outputs=[text_output, image_output]\n", + " )\n", + "\n", + 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