rtila-assistant-lite-1.5

🤖 RTILA Assistant Lite 1.5

One model to rule them all — fine-tuned on Qwen3.5-9B, the next-gen hybrid architecture with Gated Delta Networks

📋 Model Description

RTILA Assistant Lite 1.5 is a unified replacement for the entire previous RTILA model family (Mini, Lite, and full Assistant). Built on Alibaba's Qwen3.5-9B — a fundamentally new architecture featuring Gated Delta Networks and hybrid attention — it delivers flagship-level quality in a single, efficient package that runs on a wide range of hardware.

🔄 Replaces All Previous Versions

✨ Why One Model?

Qwen3.5-9B's hybrid Gated Delta Net + sparse MoE architecture is so efficient that a single 9B model now matches or exceeds the quality of the old 14B model while running in a fraction of the memory. There's no longer a reason to maintain three separate variants.

Capabilities

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📦 Model Specifications

What's New in the Base Model

Qwen3.5-9B is not just an incremental update — it's a new architecture:

• Gated Delta Networks: A hybrid layout of linear attention (DeltaNet) and standard attention layers for high-throughput inference with lower latency
• Unified Vision-Language Foundation: Early fusion training on multimodal tokens
• 201 Language Support: Massively expanded multilingual coverage
• Native 262K Context: The base model supports up to 262,144 tokens natively

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💻 Hardware Requirements

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🚀 Quick Start

Option 1: Ollama (Easiest)

# Run directly from Hugging Face
ollama run hf.co/rtila-corporation/rtila-assistant-lite-1.5:Q4_K_M

Or create a custom Modelfile:

FROM hf.co/rtila-corporation/rtila-assistant-lite-1.5:Q4_K_M

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-1.5 -f Modelfile
ollama run rtila-1.5

Option 2: LM Studio

1. Download LM Studio
2. Search for rtila-corporation/rtila-assistant-lite-1.5
3. Download Q4_K_M (~6 GB)
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-1.5 \
  qwen3.5-9b-Q4_K_M.gguf --local-dir ./models

# Run interactive chat
./llama-cli -m ./models/qwen3.5-9b-Q4_K_M.gguf \
  -p "Scrape product prices from an e-commerce site" \
  --ctx-size 2048 --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/qwen3.5-9b-Q4_K_M.gguf",
    n_ctx=2048,
    n_gpu_layers=-1,  # Use GPU/Metal if available, 0 for CPU only
)

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'])

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⚙️ Recommended Generation Settings

Based on Qwen3.5's official recommendations for non-thinking (instruct) mode:

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

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💬 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

Training Hardware

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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🔗 Links

Previous Models (Superseded)

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📄 License

Apache 2.0

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🙏 Acknowledgments

• Built on Qwen3.5-9B by Alibaba
• Fine-tuned with Unsloth
• Quantized with llama.cpp