cloud-expert-qwen-v1-1

Cloud Expert Qwen V1 1

This is version 1.1 of Qwen 2.5-Coder 1.5B fine-tuned for cloud computing & IaC

Fine-tuned on comprehensive cloud computing, Infrastructure as Code, containerization, and Linux system administration documentation.

🎯 What This Model Knows

• Cloud Platforms: Azure, AWS, GCP
• Infrastructure as Code: Terraform, CloudFormation, ARM templates
• Containers & Orchestration: Docker, Kubernetes
• Linux: System administration, troubleshooting, networking
• DevOps: CI/CD, monitoring, security best practices

📊 Training Details

• Base Model: Qwen/Qwen2.5-Coder-1.5B-Instruct
• Training Method: LoRA (Low-Rank Adaptation)
• Training Data: 149 examples (previously 43 examples)
  • Manual Q&A pairs on cloud services
  • Scraped official documentation (Azure, Docker, Kubernetes, etc.)
  • Linux troubleshooting guides
  • [NEW] Mermaid Diagram relating to Cloud Infrastructure
• Training Time: ~20-30 minutes on RTX 3070
• Trainable Parameters: ~2% (LoRA efficient training)

🚀 Quick Start

Option 1: Use with Transformers (Python)

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

# Load base model
base_model = "Qwen/Qwen2.5-Coder-1.5B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

# Load fine-tuned LoRA adapters
model = PeftModel.from_pretrained(model, "jsdjsdequinia/cloud-expert-qwen-v1-1/lora-adapters")
tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)

# Ask a question
question = "How do I troubleshoot SSH connection issues on Linux?"
prompt = f"<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant\n"

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Option 2: Use with Ollama (Recommended for CPU)

Perfect for laptops without GPU!

1. Install Ollama: https://ollama.ai/download

2. Download the model:

   huggingface-cli download jsdjsdequinia/cloud-expert-qwen-v1-1 cloud-expert-qwen-q8_0.gguf --local-dir ./
   huggingface-cli download jsdjsdequinia/cloud-expert-qwen-v1-1 Modelfile --local-dir ./

1. Create Ollama model:

   ollama create cloud-expert -f Modelfile

1. Run it:

   ollama run cloud-expert

1. Or use in code:

   import ollama
   
   response = ollama.chat(model='cloud-expert', messages=[
       {'role': 'user', 'content': 'What is Azure Virtual Machine?'}
   ])
   print(response['message']['content'])

📦 Available Formats

💡 Example Questions

- What is Microsoft Azure?
- How do I deploy a Docker container?
- Explain Terraform state management
- How do I troubleshoot disk usage on Linux?
- Compare Azure VM vs AWS EC2
- What are Kubernetes best practices?
- How do I configure a Linux firewall?

🖥️ System Requirements

For Training:

• GPU with 8GB+ VRAM
• 16GB RAM
• CUDA 12.1+

For Inference:

With Transformers (GPU):

• GPU with 4GB+ VRAM
• 8GB RAM

With Ollama (CPU - Recommended for work laptops):

• Any modern CPU
• 4GB RAM
• No GPU needed! ✅

⚡ Performance

🎓 Use Cases

✅ Learning cloud technologies
✅ Quick reference for DevOps tasks
✅ Understanding IaC best practices
✅ Linux troubleshooting assistance
✅ Comparing cloud services
✅ Interview preparation

⚠️ Limitations

• Knowledge cutoff: Training data as of 2024
• May not reflect very recent service updates
• Best for general concepts and established practices
• Always verify critical production decisions with official docs
• Not a replacement for hands-on experience

📜 License

Apache 2.0 - Free for commercial and personal use

🙏 Credits

• Base model: Qwen/Qwen2.5-Coder-1.5B-Instruct
• Fine-tuned by: jsdjsdequinia
• Documentation sources: Microsoft Azure, Docker, Kubernetes, DigitalOcean, HashiCorp

🐛 Feedback

Found an issue or have suggestions? Feel free to open an issue on the model page!

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