Instructions to use jsdjsdequinia/cloud-expert-qwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jsdjsdequinia/cloud-expert-qwen with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jsdjsdequinia/cloud-expert-qwen",
	filename="cloud-expert-qwen-q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use jsdjsdequinia/cloud-expert-qwen with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jsdjsdequinia/cloud-expert-qwen:Q8_0
# Run inference directly in the terminal:
llama-cli -hf jsdjsdequinia/cloud-expert-qwen:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jsdjsdequinia/cloud-expert-qwen:Q8_0
# Run inference directly in the terminal:
llama-cli -hf jsdjsdequinia/cloud-expert-qwen:Q8_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf jsdjsdequinia/cloud-expert-qwen:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf jsdjsdequinia/cloud-expert-qwen:Q8_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf jsdjsdequinia/cloud-expert-qwen:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jsdjsdequinia/cloud-expert-qwen:Q8_0

Use Docker

docker model run hf.co/jsdjsdequinia/cloud-expert-qwen:Q8_0

LM Studio
Jan
Ollama
How to use jsdjsdequinia/cloud-expert-qwen with Ollama:
```
ollama run hf.co/jsdjsdequinia/cloud-expert-qwen:Q8_0
```

Unsloth Studio new

How to use jsdjsdequinia/cloud-expert-qwen with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jsdjsdequinia/cloud-expert-qwen to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jsdjsdequinia/cloud-expert-qwen to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jsdjsdequinia/cloud-expert-qwen to start chatting

Pi new

How to use jsdjsdequinia/cloud-expert-qwen with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf jsdjsdequinia/cloud-expert-qwen:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "jsdjsdequinia/cloud-expert-qwen:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use jsdjsdequinia/cloud-expert-qwen with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf jsdjsdequinia/cloud-expert-qwen:Q8_0

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default jsdjsdequinia/cloud-expert-qwen:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use jsdjsdequinia/cloud-expert-qwen with Docker Model Runner:
```
docker model run hf.co/jsdjsdequinia/cloud-expert-qwen:Q8_0
```

Lemonade

How to use jsdjsdequinia/cloud-expert-qwen with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jsdjsdequinia/cloud-expert-qwen:Q8_0

Run and chat with the model

lemonade run user.cloud-expert-qwen-Q8_0

List all available models

lemonade list

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)

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!

Install Ollama: https://ollama.ai/download
Download the model:

   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 ./

Create Ollama model:

   ollama create cloud-expert -f Modelfile

Run it:

   ollama run cloud-expert

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

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
Fine-tuned by: jsdjsdequinia
Documentation sources: Microsoft Azure, Docker, Kubernetes, DigitalOcean, HashiCorp