Instructions to use lakhera2023/mini-devops-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lakhera2023/mini-devops-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="lakhera2023/mini-devops-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("lakhera2023/mini-devops-7B", dtype="auto")

PEFT
How to use lakhera2023/mini-devops-7B with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lakhera2023/mini-devops-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lakhera2023/mini-devops-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lakhera2023/mini-devops-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lakhera2023/mini-devops-7B

SGLang

How to use lakhera2023/mini-devops-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "lakhera2023/mini-devops-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lakhera2023/mini-devops-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "lakhera2023/mini-devops-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lakhera2023/mini-devops-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lakhera2023/mini-devops-7B with Docker Model Runner:
```
docker model run hf.co/lakhera2023/mini-devops-7B
```

lakhera2023 commited on May 24, 2025

Commit

6db9aa7

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@@ -16,7 +16,7 @@ license: apache-2.0
 # mini-DevOpsGPT-7B
-A specialized DevOps assistant for answering DevOps-related, Linux system administration, Docker, and questions.
 ## Model Details

 # 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