fix_colab_cloudflared.py

#!/usr/bin/env python3
"""Fix Colab — use cloudflared for tunnel (free, no auth) + verify server starts."""
import subprocess, os, json

TOKEN = "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"
subprocess.run(["git", "clone", f"https://{TOKEN}@github.com/ticketguy/littlefig.git", "/app/littlefig"], check=True)
os.chdir("/app/littlefig")
subprocess.run(["git", "config", "user.name", "0xticketguy"], check=True)
subprocess.run(["git", "config", "user.email", "0xticketguy@harboria.dev"], check=True)

colab = {
    "nbformat": 4,
    "nbformat_minor": 0,
    "metadata": {
        "colab": {"provenance": [], "gpuType": "T4"},
        "kernelspec": {"name": "python3", "display_name": "Python 3"},
        "accelerator": "GPU"
    },
    "cells": [
        {"cell_type": "markdown", "metadata": {}, "source": [
            "# 🍐 Little Fig Studio\n",
            "\n",
            "**Train any LLM on any hardware.** Select your model, configure, launch.\n",
            "\n",
            "| Feature | Result |\n",
            "|---|---|\n",
            "| Quantization | Beats NF4 on 156/156 layers |\n",
            "| GPU Speed | 7× faster than BnB NF4 |\n",
            "| CPU Training | 1.1B model in 400MB RAM |\n",
            "| Optimizer | FigMeZO (−18.6%, original research) |\n",
            "\n",
            "**Run all cells below ↓**"
        ]},
        {"cell_type": "code", "metadata": {}, "source": [
            "# Step 1: Install\n",
            "!git clone https://github.com/ticketguy/littlefig.git /content/littlefig --quiet 2>/dev/null || (cd /content/littlefig && git pull --quiet)\n",
            "!cd /content/littlefig && pip install -q -e \".[train]\"\n",
            "!pip install -q uvicorn fastapi python-multipart websockets\n",
            "\n",
            "# Download cloudflared (free tunnel, no signup)\n",
            "!wget -q https://github.com/cloudflare/cloudflared/releases/latest/download/cloudflared-linux-amd64 -O /usr/local/bin/cloudflared\n",
            "!chmod +x /usr/local/bin/cloudflared\n",
            "\n",
            "import torch, sys\n",
            "sys.path.insert(0, '/content/littlefig/src')\n",
            "print(f'\\n✅ Installed | PyTorch {torch.__version__} | GPU: {torch.cuda.get_device_name() if torch.cuda.is_available() else \"CPU\"}')"
        ], "execution_count": None, "outputs": []},
        {"cell_type": "code", "metadata": {}, "source": [
            "# Step 2: Launch Little Fig Studio\n",
            "import subprocess, time, threading, re\n",
            "\n",
            "# Start the web server\n",
            "server = subprocess.Popen(\n",
            "    ['python', '-m', 'uvicorn', 'little_fig.web.server:app',\n",
            "     '--host', '0.0.0.0', '--port', '8888'],\n",
            "    cwd='/content/littlefig/src',\n",
            "    stdout=subprocess.PIPE, stderr=subprocess.PIPE\n",
            ")\n",
            "time.sleep(3)\n",
            "\n",
            "# Check if server is running\n",
            "import urllib.request\n",
            "try:\n",
            "    urllib.request.urlopen('http://localhost:8888', timeout=2)\n",
            "    print('✅ Server running on port 8888')\n",
            "except:\n",
            "    # Server might return non-200 but still be alive\n",
            "    print('⚠️ Server may not have started correctly. Check errors below:')\n",
            "    try:\n",
            "        err = server.stderr.read(1000).decode()\n",
            "        print(err[:500])\n",
            "    except:\n",
            "        pass\n",
            "\n",
            "# Start cloudflared tunnel (free, no account needed)\n",
            "tunnel = subprocess.Popen(\n",
            "    ['cloudflared', 'tunnel', '--url', 'http://localhost:8888'],\n",
            "    stdout=subprocess.PIPE, stderr=subprocess.PIPE\n",
            ")\n",
            "time.sleep(5)\n",
            "\n",
            "# Read tunnel URL from stderr\n",
            "tunnel_url = None\n",
            "try:\n",
            "    output = tunnel.stderr.read(4096).decode()\n",
            "    urls = re.findall(r'https://[a-z0-9-]+\\.trycloudflare\\.com', output)\n",
            "    if urls:\n",
            "        tunnel_url = urls[0]\n",
            "except:\n",
            "    pass\n",
            "\n",
            "if tunnel_url:\n",
            "    print(f'\\n🍐 Little Fig Studio is LIVE!')\n",
            "    print(f'\\n   👉 {tunnel_url}')\n",
            "    print(f'\\n   Open the link above in your browser.')\n",
            "    print(f'   Select model → Configure → Train')\n",
            "else:\n",
            "    print('\\n⚠️ Tunnel failed. Using iframe fallback...')\n",
            "    from IPython.display import IFrame, display\n",
            "    # Try Colab proxy\n",
            "    from google.colab.output import eval_js\n",
            "    proxy_url = eval_js(\"google.colab.kernel.proxyPort(8888)\")\n",
            "    print(f'   Try: {proxy_url}')\n",
            "    display(IFrame(src=str(proxy_url), width='100%', height=700))\n",
            "\n",
            "print('\\n   Keep this cell running to keep the server alive.')"
        ], "execution_count": None, "outputs": []},
        {"cell_type": "markdown", "metadata": {}, "source": [
            "---\n",
            "## Alternative: Python API (no UI)\n",
            "\n",
            "Change `MODEL` to any HuggingFace model."
        ]},
        {"cell_type": "code", "metadata": {}, "source": [
            "from little_fig.engine import FigModel, FigTrainer, FigTrainingConfig\n",
            "\n",
            "# === YOUR MODEL HERE ===\n",
            "MODEL = 'TinyLlama/TinyLlama-1.1B-Chat-v1.0'\n",
            "# MODEL = 'google/gemma-3-4b-it'\n",
            "# MODEL = 'Qwen/Qwen2.5-1.5B'\n",
            "# MODEL = 'microsoft/phi-2'\n",
            "\n",
            "model = FigModel.from_pretrained(MODEL, lora_r=16, lora_alpha=32, shared_codebook=True)\n",
            "print(f'✅ {MODEL} loaded | Trainable: {sum(p.numel() for p in model.parameters() if p.requires_grad):,}')"
        ], "execution_count": None, "outputs": []},
        {"cell_type": "code", "metadata": {}, "source": [
            "# Train\n",
            "config = FigTrainingConfig(\n",
            "    num_epochs=1,\n",
            "    learning_rate=2e-4,\n",
            "    max_seq_length=256,\n",
            "    batch_size=2,\n",
            "    gradient_accumulation_steps=4,\n",
            "    logging_steps=5,\n",
            "    use_packing=True,\n",
            ")\n",
            "trainer = FigTrainer(model, config)\n",
            "trainer.load_dataset('tatsu-lab/alpaca', max_samples=200)\n",
            "trainer.train()\n",
            "model.save_adapter('./my_adapter')\n",
            "print('\\n✅ Training complete! Adapter saved.')"
        ], "execution_count": None, "outputs": []},
        {"cell_type": "markdown", "metadata": {}, "source": [
            "---\n",
            "*0xticketguy / Harboria Labs | AGPL-3.0*"
        ]}
    ]
}

with open("Little_Fig_Colab.ipynb", "w") as f:
    json.dump(colab, f, indent=2)

subprocess.run(["git", "add", "-A"], check=True)
subprocess.run(["git", "commit", "-m",
    "Fix Colab: cloudflared tunnel (free, no signup) + fallback to Colab proxy\n\n"
    "- Uses cloudflared (Cloudflare's free tunnel) instead of ngrok\n"
    "- No account needed, no auth token\n"
    "- Falls back to google.colab.output.eval_js proxy if tunnel fails\n"
    "- Checks if server is actually running before tunneling\n"
    "- Shows errors if server fails to start"], check=True)
subprocess.run(["git", "push", "origin", "main"], check=True)
print("✅ Colab fixed with cloudflared tunnel")