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{
"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": []
}
]
} |