rtila-corporation/rtila-assistant

🤖 RTILA Assistant

Full-powered fine-tuned AI model for generating automation configurations for the RTILA Automation Engine

📋 Model Description

RTILA Assistant is the standard model in the RTILA family, fine-tuned from Qwen3-14B for maximum quality. It generates JSON automation configurations for the RTILA Automation Engine with the highest accuracy and complexity handling.

🔄 Choose Your Version

Model	Base	GGUF Size	Min RAM	Best For
RTILA Assistant (this)	Qwen3-14B	~9 GB	16 GB	🏆 Maximum quality, complex automations
RTILA Assistant Lite	Qwen3-8B	~5 GB	8 GB	Balanced performance, mid-range devices
RTILA Assistant Mini	Qwen3-4B	~2.5 GB	6 GB	✅ Mac M1 8GB, low VRAM, CPU inference

Capabilities

Category	Description
🌐 Navigation & Interaction	Click, scroll, type, wait, handle popups, multi-tab workflows
📊 Data Extraction	CSS/XPath selectors, tables, lists, nested data, pagination
🔄 Logic & Flow	Loops, conditionals, error handling, retry patterns
🔗 Triggers & Integrations	Webhooks, PostgreSQL, MySQL, Slack, email notifications
📝 Variables & Substitution	Dynamic values, data transformations, regex patterns
🛠️ Advanced Scripting	Custom JavaScript execution, page analysis, DOM manipulation

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📦 Model Specifications

Property	Value
Base Model	Qwen3-14B
Format	GGUF Q4_K_M
Size	~9 GB
Context Length	1536 tokens

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💻 Hardware Requirements

Hardware	Supported	Notes
GPU (16GB+ VRAM)	✅ Recommended	RTX 4090, RTX 3090, A100
GPU (12GB VRAM)	✅ Works	RTX 4070 Ti, RTX 3080 12GB
GPU (8GB VRAM)	⚠️ Tight	RTX 3060, RTX 4060 - may need offloading
Apple Silicon 16GB+	✅ Works	M1/M2/M3 Pro/Max with 16GB+ unified memory
Apple Silicon 8GB	❌ Too small	Use Mini instead
CPU-only	⚠️ Slow	16GB+ RAM required, expect slow inference

💡 Don't have 16GB? Try RTILA Assistant Lite (8GB) or Mini (6GB)

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🚀 Quick Start

Option 1: Ollama (Easiest)

ollama run hf.co/rtila-corporation/rtila-assistant:Q4_K_M

Option 2: LM Studio

1. Download LM Studio
2. Search for rtila-corporation/rtila-assistant
3. Download Q4_K_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 \
  rtila-assistant.Q4_K_M.gguf --local-dir ./models

# Run interactive chat
./llama-cli -m ./models/rtila-assistant.Q4_K_M.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(
    model_path="./models/rtila-assistant.Q4_K_M.gguf",
    n_ctx=1536,
    n_gpu_layers=-1,  # Use GPU if available
)

response = llm.create_chat_completion(
    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

Parameter	Value
Temperature	0.7
Top-P	0.8
Top-K	20
Min-P	0

---

💬 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 execute_script and run_script?

Output:

execute_script and run_script 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

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🏋️ Training Details

Parameter	Value
Base Model	unsloth/Qwen3-14B
Method	QLoRA (4-bit)
LoRA Rank	64
LoRA Alpha	128
Context Length	1536 tokens
Training Examples	~400
Epochs	4 (with early stopping)
Learning Rate	2e-5
Thinking Mode	Disabled

Training Data

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

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📝 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:
1. Generate complete JSON configurations for web automation tasks
2. Define datasets with selectors, properties, and transformations
3. Configure navigation, extraction, loops, and conditionals
4. Set up triggers for webhooks, databases, and integrations
5. 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

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🔗 Model Family

Model	Link	Best For
RTILA Assistant (this)	huggingface.co/rtila-corporation/rtila-assistant	Maximum quality
RTILA Assistant Lite	huggingface.co/rtila-corporation/rtila-assistant-lite	Mid-range devices
RTILA Assistant Mini	huggingface.co/rtila-corporation/rtila-assistant-mini	Mac M1 8GB, low VRAM

RTILA Platform: rtila.com

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📄 License

Apache 2.0

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🙏 Acknowledgments

• Built on Qwen3-14B by Alibaba
• Fine-tuned with Unsloth
• Quantized with llama.cpp