add markdown

week1/community-contributions/wk1-day1-deepseek-stream-summarize.ipynb

@@ -2,16 +2,53 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "a767b6bc-65fe-42b2-988f-efd54125114f",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/markdown": [
+       "```markdown\n",
+       "# Summary of \"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning\"\n",
+       "\n",
+       "## Overview\n",
+       "The paper introduces **DeepSeek-R1**, a first-generation reasoning model developed by DeepSeek-AI. The model is designed to enhance reasoning capabilities in large language models (LLMs) using reinforcement learning (RL). Two versions are presented:\n",
+       "- **DeepSeek-R1-Zero**: A model trained via large-scale RL without supervised fine-tuning (SFT), showcasing strong reasoning abilities but facing challenges like poor readability and language mixing.\n",
+       "- **DeepSeek-R1**: An improved version incorporating multi-stage training and cold-start data before RL, achieving performance comparable to OpenAI's models on reasoning tasks.\n",
+       "\n",
+       "## Key Contributions\n",
+       "- Open-sourcing of **DeepSeek-R1-Zero**, **DeepSeek-R1**, and six dense models (1.5B, 7B, 8B, 14B, 32B, 70B) distilled from DeepSeek-R1 based on Qwen and Llama architectures.\n",
+       "- The models are made available to support the research community.\n",
+       "\n",
+       "## Community Engagement\n",
+       "- The paper has been widely discussed and recommended, with 216 upvotes and 45 models citing it.\n",
+       "- Additional resources, including a video review and articles, are available through external links provided by the community.\n",
+       "\n",
+       "## Related Research\n",
+       "The paper is part of a broader trend in enhancing LLMs' reasoning abilities, with related works such as:\n",
+       "- **Improving Multi-Step Reasoning Abilities of Large Language Models with Direct Advantage Policy Optimization (2024)**\n",
+       "- **Offline Reinforcement Learning for LLM Multi-Step Reasoning (2024)**\n",
+       "- **Reasoning Language Models: A Blueprint (2025)**\n",
+       "\n",
+       "## Availability\n",
+       "- The paper and models are accessible on [GitHub](https://github.com/deepseek-ai/DeepSeek-R1) and the [arXiv page](https://arxiv.org/abs/2501.12948).\n",
+       "```"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Markdown object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import os\n",
     "import requests\n",
     "from dotenv import load_dotenv\n",
     "from bs4 import BeautifulSoup\n",
-    "from IPython.display import Markdown, display\n",
+    "from IPython.display import Markdown, display, clear_output\n",
     "from openai import OpenAI\n",
     "import time\n",
     "\n",
@@ -83,9 +120,11 @@
     "    for chunk in response:\n",
     "        if chunk.choices[0].delta.content:  # Check if there's content in the chunk\n",
     "            accumulated_content += chunk.choices[0].delta.content  # Append the chunk to the accumulated content\n",
+    "            clear_output(wait=True)  # Clear the previous output\n",
+    "            display(Markdown(accumulated_content))  # Display the updated content\n",
     "    \n",
-    "    # Display the accumulated content as a single Markdown block\n",
+    "    # # Final display (optional, as the loop already displays the content)\n",
-    "    display(Markdown(accumulated_content))\n",
+    "    # display(Markdown(accumulated_content))\n",
     "\n",
     "def display_summary():\n",
     "    url = str(input(\"Enter the URL of the website you want to summarize: \"))\n",