Text Generation
MLX
Safetensors
English
Swahili
llama
secops
devsecops
cybersecurity
sast
stride
terraform
swahili
conversational
Instructions to use Bur3hani/MuchKnow-SecOps-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Bur3hani/MuchKnow-SecOps-8B with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("Bur3hani/MuchKnow-SecOps-8B") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use Bur3hani/MuchKnow-SecOps-8B with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Bur3hani/MuchKnow-SecOps-8B"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Bur3hani/MuchKnow-SecOps-8B" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Bur3hani/MuchKnow-SecOps-8B", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
MuchKnow-SecOps-8B 🛡️🔐
MuchKnow-SecOps-8B is an expert Cybersecurity & DevSecOps specialist model fine-tuned on top of deepseek-ai/DeepSeek-R1-Distill-Llama-8B.
Developed for MuchKnow (muchknow.com) and copyrighted to BuruOps (buruops.com), this model provides automated security audits, SAST/DAST analysis, Threat Modeling, infrastructure hardening, and secure CI/CD pipeline designs with explanations available in both English and Kiswahili 🇹🇿.
🔒 Specialized Security Capabilities
- Automated DevSecOps Pipeline Authoring:
- Hardened GitHub Actions, GitLab CI, and Bitbucket Pipelines incorporating Bandit (Python SAST), TruffleHog (Secrets detection), and Trivy (Container image & dependency vulnerability scanning).
- Threat Modeling & Architectural Assessments:
- Structured STRIDE framework (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege) analysis for Kubernetes (EKS, GKE, AKS), AWS, GCP, and cloud-native microservices.
- Infrastructure as Code (IaC) Security Hardening:
- Production-ready Terraform (HCL) modules enforcing S3 public access block, default KMS encryption, TLS transport policies, IAM least-privilege policies, and VPC Service Controls.
- AppSec & Vulnerability Remediation:
- Actionable code patches for SQL Injection (SQLi), Cross-Site Scripting (XSS), CSRF, and broken authorization (BOLA/IDOR), with explanations in English and Kiswahili.
🚀 Quickstart Usage
Using mlx_lm (Apple Silicon Mac)
from mlx_lm import load, generate
model, tokenizer = load("Bur3hani/MuchKnow-SecOps-8B")
prompt = """Below is a technical query in Cybersecurity and DevSecOps. Provide an accurate, secure, and detailed response in English (or Kiswahili if requested).
### Instruction:
Write a secure GitHub Actions workflow for a Python application that includes SAST scanning with Bandit, secrets detection with TruffleHog, and container image scanning with Trivy.
### Response:
"""
response = generate(model, tokenizer, prompt=prompt, max_tokens=1024)
print(response)
📜 Copyright & License
- Copyright: © 2026 BuruOps (buruops.com) & MuchKnow (muchknow.com). All Rights Reserved.
- Base Model License: Derived from DeepSeek-R1-Distill-Llama-8B.
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Model size
8B params
Tensor type
BF16
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Hardware compatibility
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Base model
deepseek-ai/DeepSeek-R1-Distill-Llama-8B