Instructions to use botbottingbot/Modular_Intelligence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use botbottingbot/Modular_Intelligence with Transformers:

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

pipe = pipeline("text-generation", model="botbottingbot/Modular_Intelligence")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("botbottingbot/Modular_Intelligence", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use botbottingbot/Modular_Intelligence with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/botbottingbot/Modular_Intelligence

SGLang

How to use botbottingbot/Modular_Intelligence 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 "botbottingbot/Modular_Intelligence" \
    --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": "botbottingbot/Modular_Intelligence",
		"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 "botbottingbot/Modular_Intelligence" \
        --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": "botbottingbot/Modular_Intelligence",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use botbottingbot/Modular_Intelligence with Docker Model Runner:
```
docker model run hf.co/botbottingbot/Modular_Intelligence
```

Modular Intelligence

Modular Intelligence is a lightweight reasoning framework built on top of a language model.
It provides Modules (task-specific lenses), Checkers (second-pass reviewers), Contracts (structured output sections), and optional Routing (automatic module selection).

The base model is GPT-2, but the architecture is model-agnostic—any LLM can be plugged in.

Features

Modules

Task-specific reasoning modes.
Examples:

Analysis Note – explanation and breakdown of concepts
Document Explainer – summaries of contracts, policies, articles
Strategy Memo – Options → Recommendation → Risks → Next Steps
System Blueprint – workflow / system design
Brainstorm – structured idea generation
Message Reply – concise responses for emails, posts, chats

Checkers

A second pass that evaluates:

correctness
clarity
missing pieces
contradictions

Contracts

Every module produces a fixed output template.
This ensures reproducible structure and reduces variance.

Router

Optional automatic module selection based on prompt classification.

Usage

Python

from app import run_module

result = run_module(
    module="StrategyMemo",
    prompt="Should we expand operations to Region X next quarter?"
)

print(result)

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