README.md

---
library_name: transformers
tags:
- devops
- linux
- system-administration
- technical-support
- question-answering
- mistral
- peft
language:
- en
pipeline_tag: text-generation
license: apache-2.0
---

# 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

```bash
pip install transformers torch accelerate peft bitsandbytes
```

### Basic Usage

```python
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

```python
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:

```bibtex
@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]