Ultimate all-in-one script that does everything in one command in AI browser

 Visual diagram/infographic showing this full pipeline from download → conversion → registration → LocalAI server → AI browser usage, which is perfect for documentation or presentations.

Perfect! 😎 Here’s the ultimate all-in-one script that does everything in one command:

• Downloads all 200+ models from your models_catalog.json
• Converts them to GGUF (quantized for low-RAM)
• Registers them with LocalAI
• Starts the LocalAI server with all models ready for API calls

All-in-One Script (all_in_one_localai.sh)

#!/bin/bash

# -----------------------------
# ALL-IN-ONE: Download, Convert, 

Register, Start LocalAI
# -----------------------------
# Requirements:
#   pip install huggingface_hub 

transformers gguf-converter localai
#   Python 3.10+
# -----------------------------

MODEL_DIR="$HOME/localai_models"
mkdir -p "$MODEL_DIR"
LOG_FILE="$MODEL_DIR/all_in_one.log"
echo "All-in-One Process started at $(date)" >

 "$LOG_FILE"

# Check Python
if ! command -v python3 &> /dev/null
then
    echo "Python3 not found. Install 

Python 3.10+ first." | tee -a "$LOG_FILE"
    exit 1
fi

# Step 1: Download + Convert + Register
python3 <<EOF
import json, os, subprocess
from huggingface_hub import snapshot_download

catalog_path = "$MODEL_DIR/models_catalog.json"
if not os.path.exists(catalog_path):
    print(f"Error: {catalog_path} not found!")
    exit(1)

with open(catalog_path) as f:
    models = json.load(f)

save_dir = "$MODEL_DIR"
os.makedirs(save_dir, exist_ok=True)

for m in models:
    model_id = m.get("model_id")
    print(f"\nDownloading: {model_id}")
    try:
        path = snapshot_download

(repo_id=model_id, cache_dir=save_dir,

 resume_download=True)
        print(f"Downloaded at: {path}")

        # GGUF conversion
        gguf_model_path = os.path.join

(save_dir, model_id.replace('/', '_') + ".gguf")
        if not os.path.exists(gguf_model_path):
            print(f"Converting 

{model_id} -> GGUF")
            subprocess.run([
                "python3", "-m", 

"gguf_converter",
                "--input", path,
                "--output", 

gguf_model_path,
                "--quantize", "int4"
            ], check=False)
            print(f"GGUF saved at:

 {gguf_model_path}")

        # Register with LocalAI
        print(f"Registering 

{gguf_model_path} with LocalAI")
        subprocess.run([
            "localai", 

"register", "--model", gguf_model_path
        ], check=False)
    except Exception as e:
        print(f"Failed: {model_id} -> {e}")
EOF

# Step 2: Start LocalAI
PORT=8080
echo "Starting LocalAI server on port $PORT..."
localai start --models "$MODEL_DIR" --port $PORT

echo "All-in-One process completed at

 $(date)" >> "$LOG_FILE"
echo "LocalAI server running on 

http://localhost:$PORT with all 

GGUF models loaded"

How to Use

1. Save as all_in_one_localai.sh
2. Make it executable:

   chmod +x all_in_one_localai.sh
   

3. Place models_catalog.json in the same folder.
4. Run it:

   ./all_in_one_localai.sh
   

5. After completion:
   • All 200+ models downloaded
   • Converted to GGUF
   • Registered with LocalAI
   • LocalAI server running on http://localhost:8080

✅ Benefits

• Single-command setup for your entire AI model library
• Optimized GGUF models for low-RAM
• Fully automated integration with LocalAI
• Ready for AI browsers, scripts, or API clients