Add Google Colab notebook for free GPU usage

jan-v1-colab.ipynb

@@ -0,0 +1,269 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# 🔬 Jan v1 Research Assistant - Google Colab Version\n",
+        "\n",
+        "Run Jan v1 (4B params) for FREE with Google Colab GPU!\n",
+        "\n",
+        "**Instructions:**\n",
+        "1. Go to Runtime → Change runtime type\n",
+        "2. Select GPU: T4 (free)\n",
+        "3. Run all cells\n",
+        "4. Use the Gradio interface at the bottom"
+      ],
+      "metadata": {
+        "id": "view-in-github"
+      }
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 1️⃣ Install Dependencies"
+      ],
+      "metadata": {
+        "id": "step1"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "install"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install transformers torch gradio accelerate bitsandbytes sentencepiece beautifulsoup4 requests -q\n",
+        "print(\"✅ Dependencies installed!\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 2️⃣ Load Jan v1 Model"
+      ],
+      "metadata": {
+        "id": "step2"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
+        "import torch\n",
+        "\n",
+        "print(\"🚀 Loading Jan v1 model...\")\n",
+        "model_name = \"janhq/Jan-v1-4B\"\n",
+        "\n",
+        "# Load with 8-bit quantization to save memory\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
+        "model = AutoModelForCausalLM.from_pretrained(\n",
+        "    model_name,\n",
+        "    torch_dtype=torch.float16,\n",
+        "    device_map=\"auto\",\n",
+        "    load_in_8bit=True\n",
+        ")\n",
+        "\n",
+        "print(\"✅ Model loaded successfully!\")\n",
+        "print(f\"Model size: {model.num_parameters()/1e9:.2f}B parameters\")"
+      ],
+      "metadata": {
+        "id": "load_model"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 3️⃣ Define Research Functions"
+      ],
+      "metadata": {
+        "id": "step3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import requests\n",
+        "from bs4 import BeautifulSoup\n",
+        "import gradio as gr\n",
+        "\n",
+        "def scrape_url(url: str) -> str:\n",
+        "    \"\"\"Scrape and extract text from URL\"\"\"\n",
+        "    try:\n",
+        "        headers = {'User-Agent': 'Mozilla/5.0'}\n",
+        "        response = requests.get(url, headers=headers, timeout=10)\n",
+        "        soup = BeautifulSoup(response.content, 'html.parser')\n",
+        "        \n",
+        "        for script in soup([\"script\", \"style\"]):\n",
+        "            script.decompose()\n",
+        "        \n",
+        "        text = soup.get_text()\n",
+        "        lines = (line.strip() for line in text.splitlines())\n",
+        "        chunks = (phrase.strip() for line in lines for phrase in line.split(\"  \"))\n",
+        "        text = ' '.join(chunk for chunk in chunks if chunk)\n",
+        "        \n",
+        "        return text[:4000]\n",
+        "    except Exception as e:\n",
+        "        return f\"Error: {str(e)}\"\n",
+        "\n",
+        "def research_assistant(query: str, context: str = \"\", temperature: float = 0.6):\n",
+        "    \"\"\"Main research function using Jan v1\"\"\"\n",
+        "    \n",
+        "    # Check if context is URL\n",
+        "    if context.startswith('http'):\n",
+        "        context = scrape_url(context)\n",
+        "    \n",
+        "    prompt = f\"\"\"You are an expert research analyst. Provide comprehensive analysis.\n",
+        "\n",
+        "Context: {context if context else 'No specific context'}\n",
+        "\n",
+        "Query: {query}\n",
+        "\n",
+        "Provide:\n",
+        "1. Key findings\n",
+        "2. Critical analysis\n",
+        "3. Supporting evidence\n",
+        "4. Follow-up questions\n",
+        "\n",
+        "Analysis:\"\"\"\n",
+        "    \n",
+        "    inputs = tokenizer(prompt, return_tensors=\"pt\", truncation=True, max_length=2048)\n",
+        "    inputs = inputs.to(model.device)\n",
+        "    \n",
+        "    with torch.no_grad():\n",
+        "        outputs = model.generate(\n",
+        "            **inputs,\n",
+        "            max_new_tokens=1024,\n",
+        "            temperature=temperature,\n",
+        "            top_p=0.95,\n",
+        "            top_k=20,\n",
+        "            do_sample=True,\n",
+        "            pad_token_id=tokenizer.eos_token_id\n",
+        "        )\n",
+        "    \n",
+        "    response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
+        "    response = response.replace(prompt, \"\").strip()\n",
+        "    \n",
+        "    return response\n",
+        "\n",
+        "print(\"✅ Functions defined!\")"
+      ],
+      "metadata": {
+        "id": "functions"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 4️⃣ Create Gradio Interface"
+      ],
+      "metadata": {
+        "id": "step4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Create Gradio interface\n",
+        "with gr.Blocks(title=\"Jan v1 Research Assistant\", theme=gr.themes.Soft()) as demo:\n",
+        "    gr.Markdown(\"\"\"\n",
+        "    # 🔬 Jan v1 Research Assistant (Google Colab)\n",
+        "    \n",
+        "    Powered by Jan-v1-4B - Running on FREE Google Colab GPU!\n",
+        "    \"\"\")\n",
+        "    \n",
+        "    with gr.Row():\n",
+        "        with gr.Column():\n",
+        "            query = gr.Textbox(\n",
+        "                label=\"Research Query\",\n",
+        "                placeholder=\"What would you like to research?\",\n",
+        "                lines=2\n",
+        "            )\n",
+        "            context = gr.Textbox(\n",
+        "                label=\"Context (text or URL)\",\n",
+        "                placeholder=\"Paste text or URL to analyze\",\n",
+        "                lines=5\n",
+        "            )\n",
+        "            temp = gr.Slider(0.1, 1.0, value=0.6, label=\"Temperature\")\n",
+        "            btn = gr.Button(\"🔍 Analyze\", variant=\"primary\")\n",
+        "        \n",
+        "        with gr.Column():\n",
+        "            output = gr.Textbox(\n",
+        "                label=\"Analysis Results\",\n",
+        "                lines=15\n",
+        "            )\n",
+        "    \n",
+        "    btn.click(\n",
+        "        research_assistant,\n",
+        "        inputs=[query, context, temp],\n",
+        "        outputs=output\n",
+        "    )\n",
+        "    \n",
+        "    gr.Examples(\n",
+        "        examples=[\n",
+        "            [\"What are the key trends in AI research?\", \"\", 0.6],\n",
+        "            [\"Analyze this article for bias\", \"https://example.com/article\", 0.4],\n",
+        "            [\"Generate research questions about climate change\", \"\", 0.7]\n",
+        "        ],\n",
+        "        inputs=[query, context, temp]\n",
+        "    )\n",
+        "\n",
+        "# Launch the interface\n",
+        "demo.launch(share=True)  # share=True creates a public link"
+      ],
+      "metadata": {
+        "id": "gradio"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## 📝 Quick Test"
+      ],
+      "metadata": {
+        "id": "test"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Test the model directly\n",
+        "test_result = research_assistant(\n",
+        "    \"What are the implications of large language models for research?\",\n",
+        "    \"Large language models have billions of parameters and can process vast amounts of text.\"\n",
+        ")\n",
+        "\n",
+        "print(\"Test Result:\")\n",
+        "print(test_result)"
+      ],
+      "metadata": {
+        "id": "test_code"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
+  ]
+}