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
```

botbottingbot commited on Nov 18, 2025

Commit

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@@ -1,3 +1,18 @@
 ---
 library_name: transformers
 license: mit

+## System-level view (60 seconds)
+This is **not** just a GPT-2 model.
+It is a small, self-contained **reasoning system** with:
+- **Modules**: task-specific lenses (Analysis Note, Strategy Memo, Document Explainer, etc.).
+- **Checkers**: second-pass reviewers that audit a module’s output.
+- **Contracts**: every module must answer in fixed sections (e.g. CONTEXT / OPTIONS / RISKS / NEXT_STEPS).
+Under the hood it still uses next-token prediction, but the **system behaviour** is:
+> free-form task → choose lens (module) → generate structured output → optional checker review.
+You can swap the engine (`gpt2`) for any stronger model. The **architecture stays the same**.
 ---
 library_name: transformers
 license: mit