mini-DevOpsGPT-7B

mini-DevOpsGPT-7B is your on-demand AI DevOps engineer, offering expert guidance across the full operations lifecycle—from Linux system administration (user & permission management, shell scripting, performance tuning) and Docker/Kubernetes containerization (optimized Dockerfiles, Helm charts, operators) to CI/CD pipeline design and troubleshooting (Jenkins, GitHub Actions, Argo CD), infrastructure-as-code (Terraform, CloudFormation, Pulumi), cloud architecture (AWS, Azure, GCP), configuration management (Ansible, Chef, Puppet), observability (Prometheus, Grafana, ELK), security best practices (secrets management, image scanning, IAM hardening), networking and service mesh (VPC, load balancers, Istio/Linkerd), serverless and event-driven patterns, and end-to-end automation—complete with concise examples and battle-tested recommendations.

Model Details

Model Description

This model is specifically trained to assist with DevOps tasks, Linux system administration, and technical troubleshooting. It provides accurate, practical answers for common infrastructure and system management questions.

Developed by: [Prashant Lakhera]
Model type: Causal Language Model (Auto-regressive)
Language(s): English
License: Apache 2.0
Training method: LoRA (Low-Rank Adaptation) with 4-bit quantization
Specialization: DevOps, Linux Administration, System Troubleshooting

Model Sources

Fine-tuning: Custom DevOps dataset

Uses

Direct Use

This model is designed for:

DevOps Q&A: Answering questions about system administration, deployment, and infrastructure
Linux Help: Providing command-line solutions and troubleshooting steps
Docker/Container Support: Assistance with containerization and orchestration
System Monitoring: Guidance on logging, monitoring, and debugging
Automation Advice: Help with scripting and workflow automation

Example Use Cases

DevOps automation for IT support
Developer productivity tools
System administration training
Technical documentation assistance
Infrastructure troubleshooting

Out-of-Scope Use

Not suitable for: Medical advice, legal guidance, financial decisions
Limitations: May not have knowledge of very recent tools or updates
Security: Should not be used for security-critical decisions without validation

How to Get Started with the Model

Installation

pip install transformers torch accelerate peft bitsandbytes

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
model_name = "your-username/mini-DevOpsGPT-7B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Example usage
question = "How to check disk space in Linux?"
inputs = tokenizer(question, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_length=inputs.input_ids.shape[1] + 100,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Chat Interface Example

def ask_devops_question(question):
    inputs = tokenizer(f"Question: {question}\n\nAnswer:", return_tensors="pt")
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_length=200,
            temperature=0.7,
            do_sample=True,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=tokenizer.eos_token_id
        )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split("Answer:")[-1].strip()

# Example questions
print(ask_devops_question("How to restart Docker service?"))
print(ask_devops_question("How to kill a process by PID?"))
print(ask_devops_question("How to view log files in real time?"))

Training Details

Training Data

The model was fine-tuned on a curated dataset of DevOps question-answer pairs covering:

Linux system administration commands
Docker and containerization
Process management
File system operations
Network troubleshooting
Log analysis and monitoring
Kubernetes

Dataset Format:

How to check disk space in Linux?

Use df -h command to check disk space usage.

Training Procedure

Training Hyperparameters

Training method: LoRA (Low-Rank Adaptation)
Quantization: 4-bit (int4) using bitsandbytes
Training regime: fp16 mixed precision

LoRA Configuration:

LoRA Rank (r): 16
LoRA Alpha: 32
LoRA Dropout: 0.05
Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Training Parameters:

Epochs: 3
Batch Size: 2 (per device)
Gradient Accumulation Steps: 4
Learning Rate: 3e-5
Optimizer: AdamW
Scheduler: Linear with warmup
Warmup Ratio: 0.1
Max Sequence Length: 1024
Max Gradient Norm: 1.0

Speeds, Sizes, Times

Training Time: ~45-60 minutes on Nvidia A10 large GPU
Model Size: ~7B parameters (base) + ~16M trainable LoRA parameters
Memory Usage: ~10GB GPU RAM during training
Final Model Size: ~2GB (with quantization and LoRA)

Performance

Example Outputs

Input: "How to check running processes in Linux?" Output: "Use ps aux command to list all running processes with detailed information including CPU and memory usage."

Input: "How to restart Docker service?" Output: "Run sudo systemctl restart docker to restart the Docker service on systemd-based systems."

Input: "How to kill a process by PID?" Output: "Use kill -9 PID to forcefully terminate a process, replacing PID with the actual process ID."

Technical Specifications

Model Architecture

Parameters: 7.24B total, ~16M trainable (LoRA)
Attention: Grouped Query Attention
Vocabulary Size: 32,000
Context Length: 8,192 tokens (base), 1,024 used in training

Compute Infrastructure

Hardware

Training: Nvidia A10 large GPU
Memory: ~10GB GPU RAM required
Inference: Compatible with consumer GPUs (8GB+ recommended)

Software

Framework: PyTorch + Transformers
Libraries: PEFT, bitsandbytes, accelerate
Quantization: 4-bit using bitsandbytes

Limitations and Bias

Known Limitations

Domain Scope: Primarily trained on Linux/Unix-based systems
Recency: Knowledge cutoff from base model training
Commands: May need verification for specific system configurations
Security: Always validate security-related commands before execution

Recommendations

Verify Commands: Test commands in safe environments first
System Specific: Adapt commands to your specific Linux distribution
Security: Review security implications of suggested commands
Updates: Check for newer versions of tools and commands

Environmental Impact

Training was conducted on Nvidia A10 large GPU infrastructure:

Hardware Type: Nvidia A10 large GPUNVIDIA T4 GPU
Hours used: ~1 hour
Compute Region: Variable (Colab auto-assignment)
Carbon Emitted: Minimal due to short training time and shared infrastructure

Citation

If you use this model, please cite:

@misc{mini-devopsgpt-7b,
  title={mini-DevOpsGPT-7B: A Model for DevOps Tasks},
  author={[lakhera2023]},
  year={2025},
  howpublished={\\url{https://huggingface.co/lakhera2023/mini-DevOpsGPT-7B}},
}

Model Card Authors

[lakhera2023/mini-DevOpsGPT-7b]

Model Card Contact

[laprashant@gmail.com]