🧠 Web0ps-4B-Instruct Model The WebOps Ollama Model is an advanced LLM (Large Language Model) specifically designed to enhance AI-driven web automation, intelligent browsing, and contextual decision-making within the WebOps framework. By integrating this cutting-edge AI model, WebOps aims to provide autonomous, adaptive, and scalable web agents capable of understanding, analyzing, and interacting with web content dynamically. Follow Our X for more

Unlike traditional rule-based automation or static web scrapers, the WebOps Ollama Model brings real-time comprehension and decision-making capabilities, allowing AI agents to navigate complex online environments intelligently.

📊 Model Architecture

Training Workflow The model was fine-tuned using parameter-efficient methods with LoRA to adapt to the Solana-specific domain. Below is a visualization of the training process:

+---------------------------+               +-------------------------+
|       Base Model          |  --- LoRA -->|  Fine-Tuned Adapter     |
|    LLaMa 3.1 8B           |               |WebOPs-2B-Instruct        |
+---------------------------+               +-------------------------+

🛠️ Installation and Usage

To install WebOps and the Ollama AI model, run the following command: $pip install webops

Setting Up Ollama Model for AI-Powered Automation

from webops import WebAgent, OllamaAI

# Initialize AI-powered WebAgent
agent = WebAgent(headless=False)
ai_model = OllamaAI()

# Open a website
agent.open("https://example.com")

# Extract and analyze content using AI
content = agent.extract_text("div.article-content")
summary = ai_model.summarize(content)

print(f"Extracted Content: {content}")
print(f"AI Summary: {summary}")

# Close the session
agent.close()

Using Ollama AI with WebOps for Advanced Web Automation

agent.open("https://news.ycombinator.com")

# Extract headlines
headlines = agent.extract_text("tr.athing span.titleline")
analysis = ai_model.summarize(headlines)

Deployment & API Usage

from flask import Flask, request, jsonify
from webops import WebAgent, OllamaAI

app = Flask(__name__)

@app.route("/analyze", methods=["POST"])
def analyze():
    data = request.json
    url = data.get("url")

    agent = WebAgent()
    ai_model = OllamaAI()

    agent.open(url)
    content = agent.extract_text("div.content")
    summary = ai_model.summarize(content)

    agent.close()
    return jsonify({"summary": summary})

if __name__ == "__main__":
    app.run(port=5000)

Handle Large-Scale Automation with Multi-Agent Execution

from multiprocessing import Pool

urls = ["https://example1.com", "https://example2.com"]

def analyze_page(url):
    agent = WebAgent()
    ai_model = OllamaAI()
    agent.open(url)
    content = agent.extract_text("body")
    summary = ai_model.summarize(content)
    agent.close()
    return summary

with Pool(4) as p:
    results = p.map(analyze_page, urls)

🎯Key Features of the WebOps Ollama Model 🚀 1. AI-Powered Autonomous Web Interaction The model enables AI agents to operate like human users, navigating websites intelligently without relying solely on predefined scripts.

✅ Understands web content dynamically, recognizing changes in HTML structure.

✅ Interacts with buttons, forms, and navigation menus seamlessly.

✅ Adapts to website layout changes, preventing automation failures.

✅ Example Use Case:

Automate multi-step workflows, such as registering accounts, logging in, and submitting forms on various websites without requiring predefined CSS selectors. 📊 2. Context-Aware Web Decision Making Traditional automation relies on hardcoded instructions, but the WebOps Ollama Model can analyze page content and make real-time decisions based on context.

✅ Extracts relevant data by understanding webpage structures, even on JavaScript-heavy sites.

✅ Determines which actions to take based on content analysis (e.g., whether a button is enabled/disabled).

✅ Detects CAPTCHAs, pop-ups, and dynamic elements, providing solutions or suggesting manual intervention.

✅ Example Use Case:

AI-powered news aggregation—extracts and summarizes the latest articles, identifies key topics, and prioritizes breaking news based on relevance. 📡 3. Enhanced Web Scraping & Data Extraction Unlike traditional web scrapers that break easily when websites change, the WebOps Ollama Model uses AI-driven parsing to extract both structured and unstructured data with precision.

✅ Extracts tables, lists, and financial reports with context awareness.

✅ Identifies key-value pairs even on dynamically generated content.

✅ Captures hidden elements such as AJAX-loaded content and dynamically inserted JavaScript elements.

✅ Example Use Case:

Automate e-commerce market analysis by extracting product prices, reviews, and availability status across multiple competitor websites.

🙌 Contributing We welcome contributions to enhance the Web0ps-4B-Instruct model. Feel free to: - Share your feedback on the HuggingFace Model Hub.

📜 License This model is licensed under the GNU Affero General Public License v3.0 (AGPLv3).

📞 Community For questions or support, reach out via: - Twitter: WebOps Ai

🤝 Acknowledgments Special thanks to the Solana ecosystem developers and the open-source community for their invaluable contributions and support.