Instructions to use YoSaNoo/Most-Dumb-Intelligence with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use YoSaNoo/Most-Dumb-Intelligence with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf YoSaNoo/Most-Dumb-Intelligence:F32 # Run inference directly in the terminal: llama cli -hf YoSaNoo/Most-Dumb-Intelligence:F32
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf YoSaNoo/Most-Dumb-Intelligence:F32 # Run inference directly in the terminal: llama cli -hf YoSaNoo/Most-Dumb-Intelligence:F32
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf YoSaNoo/Most-Dumb-Intelligence:F32 # Run inference directly in the terminal: ./build/bin/llama cli -hf YoSaNoo/Most-Dumb-Intelligence:F32
Use Docker
docker model run hf.co/YoSaNoo/Most-Dumb-Intelligence:F32
- Llama new
- LM Studio
- Jan
- vLLM
How to use YoSaNoo/Most-Dumb-Intelligence with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "YoSaNoo/Most-Dumb-Intelligence" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "YoSaNoo/Most-Dumb-Intelligence", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/YoSaNoo/Most-Dumb-Intelligence:F32
- Ollama
How to use YoSaNoo/Most-Dumb-Intelligence with Ollama:
ollama run hf.co/YoSaNoo/Most-Dumb-Intelligence:F32
- Unsloth Desktop
- Docker Model Runner
How to use YoSaNoo/Most-Dumb-Intelligence with Docker Model Runner:
docker model run hf.co/YoSaNoo/Most-Dumb-Intelligence:F32
- Lemonade
How to use YoSaNoo/Most-Dumb-Intelligence with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull YoSaNoo/Most-Dumb-Intelligence:F32
Run and chat with the model
lemonade run user.Most-Dumb-Intelligence-F32
List all available models
lemonade list
- Atomic Chat
Most-Dumb-Intelligence
Welcome to Most-Dumb-Intelligence, a language model that proudly lives up to its name. If you are looking for groundbreaking reasoning, quantum physics calculations, or deep philosophical answers β you are definitely in the wrong place.
This model is built on a custom, home-grown neural network architecture developed from scratch (ignore the Hugging Face automated "Llama" tag, it has nothing to do with Llama).
With a whopping 0.1B parameters, it is exceptionally lightweight, remarkably fast, and completely unfiltered by complex logic.
Model Details
- Developed by: YoSaNoo
- Architecture: Custom / Proprietary (Built from scratch)
- Parameters: 0.1B
- Format: GGUF (F32 / ~401 MB)
- Tags:
gguf,conversational - License: Apache-2.0
Features & "Intelligence"
- Pure Simplicity: No overhead, no overthinking. It answers fast because it doesn't spend time reasoning.
- Custom Architecture: Built from the ground up to explore alternative neural network designs outside the standard transformer meta.
- Perfect for Memes: Ideal for generating absurd, chaotic, or unexpectedly funny outputs.
- Ultra-Low Hardware Requirements: Runs on literally anything that can load a 400 MB file.
Intended Uses & Limitations
Best Used For:
- Testing custom architecture deployment pipelines in GGUF format.
- Entertainment, chaos generation, and stress-testing your patience.
- Academic curiosity on how models behave at the absolute lower limits of parameter size and complexity.
Limitations:
- IQ Level: Strictly single-digit.
- Expect hallucinated facts, syntax breakdown, and absolute nonsense. Treat it as a feature, not a bug!
How to Use
Since this uses a custom architecture, standard Llama loaders might fail. You will need a loader or script capable of parsing this specific custom network configuration.
Local Loading (Conceptual Example)
# Ensure your local environment has the custom inference script for YoSaNoo's architecture
from yosanoo_inference import CustomModelLoader
model = CustomModelLoader.load_gguf("./most-dumb-intelligence.gguf")
response = model.generate("Why is the sky blue?", max_tokens=20)
print(response)
License
This project is open-source and licensed under the Apache License 2.0.
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