Instructions to use tflux2011/contextual-engineering-framework with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tflux2011/contextual-engineering-framework with Transformers:

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

pipe = pipeline("text-generation", model="tflux2011/contextual-engineering-framework")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tflux2011/contextual-engineering-framework", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tflux2011/contextual-engineering-framework with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tflux2011/contextual-engineering-framework"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tflux2011/contextual-engineering-framework",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tflux2011/contextual-engineering-framework

SGLang

How to use tflux2011/contextual-engineering-framework 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 "tflux2011/contextual-engineering-framework" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tflux2011/contextual-engineering-framework",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "tflux2011/contextual-engineering-framework" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tflux2011/contextual-engineering-framework",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tflux2011/contextual-engineering-framework with Docker Model Runner:
```
docker model run hf.co/tflux2011/contextual-engineering-framework
```

contextual-engineering-framework / README.md

tflux2011

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	---
	library_name: transformers
	tags:
	- agents
	- offline-first
	- edge-computing
	- context-aware
	- global-south
	- low-resource-nlp
	license: mit
	language:
	- en
	pipeline_tag: text-generation
	---



	# Contextual Engineering Patterns: Architecting Adaptable AI Agents

	[![License: CC BY 4.0](https://img.shields.io/badge/License-CC%20BY%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)
	[![Status: Open Access](https://img.shields.io/badge/Status-Open%20Access-green.svg)]()
	[![Paper: AfricArXiv](https://img.shields.io/badge/Paper-Read%20Preprint-red)](https://africarxiv.ubuntunet.net/items/2af79f5d-ce68-4050-8b25-3bc9128c7232)
	[![Book: Published](https://img.shields.io/badge/Book-Read%20Full%20Text-blue)](https://zenodo.org/records/18005435)

	> *Reference implementations for the architectural patterns defined in the book "Contextual Engineering: Architecting Adaptable AI Agents for the Real World"* by Tobi Lekan Adeosun.**

	## 📖 Overview

	Standard AI agents are designed for the "Abundance Baseline" of Silicon Valley—perfect internet, unlimited power, and institutional trust. When deployed in the Global South, these agents fail due to the "Agentic Gap" between their reasoning capabilities and environmental realities.

	This repository contains the Python reference implementations for the three core adaptation layers introduced in the book:
	1. Infrastructure Adapter: Handling offline states and compute scarcity.
	2. Cultural Adapter: Managing semantic drift and high-context communication.
	3. Safety Adapter: Enforcing constitutional guardrails and Human-in-the-Loop (HITL) workflows.

	## ⚡ Quick Start (Hybrid Router)

	How to use the Infrastructure Adapter to route traffic based on connectivity:

	```python
	from src.infrastructure.inference_router import HybridRouter

	# Initialize router with cost/latency preferences
	router = HybridRouter(preference="economy", offline_fallback=True)

	# The router automatically checks network status (N(t))
	model_choice = router.select_model(
	prompt="Summarize this contract",
	complexity_score=0.85
	)

	print(f"Routing to: {model_choice}")
	# Output: "Llama-3-8B-Local" (if offline) or "GPT-4o" (if online)

	## 📂 Repository Structure

	The code is organized by the "Adapter Layer" it serves, matching the chapters of the manuscript.

	```text
	├── src
	│ ├── infrastructure
	│ │ ├── sync_manager.py # (Chapter 3) The "Sync-Later" Architecture & Offline Queue
	│ │ └── inference_router.py # (Chapter 4) The Hybrid Router (Local vs. Cloud)
	│ ├── safety
	│ │ ├── sentinel.py # (Chapter 9) Constitutional Safety Checks & Kill Switches
	│ │ └── escalation_ladder.py # (Chapter 10) Human-in-the-Loop Risk Evaluation Logic
	│ └── culture
	│ └── context_injector.py # (Chapter 6) Dynamic Few-Shot Prompting logic
	└── README.md


	## Citation

	If you use this framework in your research, please cite the associated whitepaper:

	```bibtex
	@article{adeosun2026contextual,
	title={Contextual Engineering: Architectural Patterns for Resilient AI Agents},
	author={Adeosun, Tobi},
	journal={AfricArXiv},
	year={2026},
	url={[https://osf.io/preprints/africarxiv/](https://osf.io/preprints/africarxiv/)[YOUR_HANDLE]}
	}