---
language: en
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
tags:
- qwen2.5
- cloud
- azure
- aws
- terraform
- docker
- kubernetes
- linux
- iac
---

# Cloud Expert Qwen

This is a version 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

## 🚀 Quick Start

### Option 1: Use with Transformers (Python)
```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/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**:
```bash
   huggingface-cli download jsdjsdequinia/cloud-expert-qwen cloud-expert-qwen-q8_0.gguf --local-dir ./
   huggingface-cli download jsdjsdequinia/cloud-expert-qwen Modelfile --local-dir ./
```

3. **Create Ollama model**:
```bash
   ollama create cloud-expert -f Modelfile
```

4. **Run it**:
```bash
   ollama run cloud-expert
```

5. **Or use in code**:
```python
   import ollama
   
   response = ollama.chat(model='cloud-expert', messages=[
       {'role': 'user', 'content': 'What is Azure Virtual Machine?'}
   ])
   print(response['message']['content'])
```

## 📦 Available Formats

| Format | Size | Use Case | Download |
|--------|------|----------|----------|
| **LoRA Adapters** | ~100MB | Fine-tuning, GPU inference | `lora-adapters/` |
| **Merged Model** | ~3GB | Full model, GPU inference | `merged-model/` |
| **GGUF (q8_0)** | ~1.5GB | CPU inference with Ollama | `*.gguf` |

## 📊 Training Details

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

## 💡 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

| Setup | Tokens/Second | Use Case |
|-------|---------------|----------|
| GPU (RTX 3070) | ~50 tok/s | Development, training |
| CPU (Ollama, 16GB RAM) | ~10-15 tok/s | Work laptop, portable |

## 🎓 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](https://huggingface.co/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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