🤖 RTILA Assistant Lite

Balanced fine-tuned AI model for mid-range devices — 8GB+ RAM systems

⚠️ Mac M1 8GB Users: While this model may run on 8GB systems, it will be tight on memory. For a smoother experience, we recommend RTILA Assistant Mini which is confirmed working on Mac M1 8GB.

Capabilities

📦 Model Specifications

💻 Hardware Requirements

💡 Memory tight? Try RTILA Assistant Mini (~2.5GB GGUF, runs smoothly on 6GB)

🚀 Quick Start

Option 1: Ollama (Easiest)

# Run directly from Hugging Face
ollama run hf.co/rtila-corporation/rtila-assistant-lite:Q4KM

Or create a custom Modelfile:

FROM hf.co/rtila-corporation/rtila-assistant-lite:Q4KM


PARAMETER temperature 0.7
PARAMETER top_p 0.8
PARAMETER top_k 20

SYSTEM """
You are RTILA Assistant, an expert AI for generating automation configurations for the RTILA Automation Engine.
"""

ollama create rtila-lite -f Modelfile
ollama run rtila-lite

Option 2: LM Studio

1. Download LM Studio
2. Search for rtila-corporation/rtila-assistant-lite
3. Download Q4K_M
4. Set parameters: Temperature=0.7, Top-P=0.8, Top-K=20
5. Start chatting!

Option 3: llama.cpp

# Download model
huggingface-cli download rtila-corporation/rtila-assistant-lite \
  rtila-assistant-lite.Q4KM.gguf --local-dir ./models

Run interactive chat
./llama-cli -m ./models/rtila-assistant-lite.Q4KM.gguf \
  -p "Scrape product prices from an e-commerce site" \
  --temp 0.7 --top-p 0.8 --top-k 20

Option 4: Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama(
    modelpath="./models/rtila-assistant-lite.Q4K_M.gguf",
    n_ctx=2048,
    ngpulayers=-1,  # Use GPU if available, 0 for CPU only
)

response = llm.createchatcompletion(
    messages=[
        {"role": "system", "content": "You are RTILA Assistant..."},
        {"role": "user", "content": "Create a config for scraping product data"}
    ],
    temperature=0.7,
    top_p=0.8,
    top_k=20,
)
print(response['choices'][0]['message']['content'])

⚙️ Recommended Generation Settings

Based on Qwen3's official recommendations for non-thinking mode:

ℹ️ Note: This model was trained with thinking mode disabled for efficient structured JSON output. It will not produce ... blocks.

💬 Example Prompts & Outputs

Example 1: Basic Extraction

Prompt:

Extract all product names and prices from the current page

Output:

{
  "settings": {
    "name": "Product Extraction"
  },
  "datasets": {
    "products": {
      "selector": ".product-item",
      "type": "list",
      "fields": {
        "name": {
          "selector": ".product-title",
          "type": "text"
        },
        "price": {
          "selector": ".product-price",
          "type": "text",
          "transform": "extractNumber"
        }
      }
    }
  },
  "commands": [
    {"action": "extract", "dataset": "products"}
  ]
}

Example 2: Pagination Loop

Prompt:

Scrape reviews from multiple pages, clicking "Next" until no more pages

Output:

{
  "settings": {
    "name": "Review Scraper with Pagination"
  },
  "datasets": {
    "reviews": {
      "selector": ".review-card",
      "type": "list",
      "fields": {
        "author": {"selector": ".reviewer-name", "type": "text"},
        "rating": {"selector": ".star-rating", "type": "attribute", "attribute": "data-rating"},
        "text": {"selector": ".review-body", "type": "text"},
        "date": {"selector": ".review-date", "type": "text"}
      }
    }
  },
  "commands": [
    {
      "action": "loop",
      "while": {"exists": ".next-page:not(.disabled)"},
      "maxIterations": 50,
      "commands": [
        {"action": "extract", "dataset": "reviews", "append": true},
        {"action": "click", "selector": ".next-page"},
        {"action": "wait", "duration": 2000}
      ]
    }
  ]
}

Example 3: Database Integration

Prompt:

After extraction, save data to PostgreSQL and send Slack notification

Output:

{
  "triggers": {
    "onComplete": [
      {
        "type": "database",
        "config": {
          "driver": "postgresql",
          "connection": "{{env.DATABASE_URL}}",
          "table": "scraped_data",
          "mode": "upsert",
          "uniqueKey": "id"
        }
      },
      {
        "type": "webhook",
        "config": {
          "url": "{{env.SLACK_WEBHOOK}}",
          "method": "POST",
          "body": {
            "text": "✅ Extraction complete: {{datasets.products.length}} items saved"
          }
        }
      }
    ]
  }
}

Example 4: Knowledge Question

Prompt:

What is the difference between executescript and runscript?

Output:

executescript and runscript serve different purposes in RTILA:


execute_script: Runs JavaScript synchronously in the page context. Returns immediately 
with the result. Best for quick DOM queries, getting values, or simple manipulations.

run_script: Executes JavaScript asynchronously with full control flow. Supports 
await, can make fetch requests, and handles complex async operations. Returns a Promise.

Use execute_script for: Reading values, checking conditions, simple DOM changes
Use run_script for: API calls, complex async workflows, operations that need to wait

🏋️ Training Details

Optimizations for Lite Version

• Higher LoRA rank (128 vs 64): Compensates for smaller base model
• Higher learning rate (2e-4 vs 2e-5): Better convergence for smaller models
• Longer context (2048 vs 1536): More headroom for complex configurations
• Thinking mode disabled: Eliminates overhead for structured output
• Rank-stabilized LoRA (rsLoRA): More stable training

Training Data

• Navigation & Interaction patterns
• Data extraction configurations
• Logic & flow control
• Triggers & integrations
• Variables & substitution
• Advanced scripting
• Error handling
• Knowledge base Q&A

📝 System Prompt

For best results, use this system prompt:

You are RTILA Assistant, an expert AI for generating automation configurations for the RTILA Automation Engine.

Your capabilities:
Generate complete JSON configurations for web automation tasks
Define datasets with selectors, properties, and transformations
Configure navigation, extraction, loops, and conditionals
Set up triggers for webhooks, databases, and integrations
Explain RTILA concepts and best practices


When generating configurations:
Always output valid JSON with proper structure
Include 'settings', 'datasets', and 'commands' sections as needed
Use appropriate selectors (CSS, XPath) for the target elements
Apply transformations when data cleaning is required


When answering questions:
Be concise and accurate
Provide examples when helpful
Reference specific RTILA features and commands

🔗 Model Family

📄 License

Apache 2.0

🙏 Acknowledgments

• Built on Qwen3-8B by Alibaba
• Fine-tuned with Unsloth
• Quantized with llama.cpp