Instructions to use build-small-hackathon/deku-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use build-small-hackathon/deku-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="build-small-hackathon/deku-gguf",
	filename="deku-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use build-small-hackathon/deku-gguf 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 build-small-hackathon/deku-gguf:F16
# Run inference directly in the terminal:
llama cli -hf build-small-hackathon/deku-gguf:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf build-small-hackathon/deku-gguf:F16
# Run inference directly in the terminal:
llama cli -hf build-small-hackathon/deku-gguf:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf build-small-hackathon/deku-gguf:F16
# Run inference directly in the terminal:
./llama-cli -hf build-small-hackathon/deku-gguf:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf build-small-hackathon/deku-gguf:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf build-small-hackathon/deku-gguf:F16

Use Docker

docker model run hf.co/build-small-hackathon/deku-gguf:F16

LM Studio
Jan

vLLM

How to use build-small-hackathon/deku-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "build-small-hackathon/deku-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "build-small-hackathon/deku-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/build-small-hackathon/deku-gguf:F16

Ollama
How to use build-small-hackathon/deku-gguf with Ollama:
```
ollama run hf.co/build-small-hackathon/deku-gguf:F16
```

Unsloth Studio

How to use build-small-hackathon/deku-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for build-small-hackathon/deku-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for build-small-hackathon/deku-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for build-small-hackathon/deku-gguf to start chatting

How to use build-small-hackathon/deku-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf build-small-hackathon/deku-gguf:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "build-small-hackathon/deku-gguf:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use build-small-hackathon/deku-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf build-small-hackathon/deku-gguf:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default build-small-hackathon/deku-gguf:F16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use build-small-hackathon/deku-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf build-small-hackathon/deku-gguf:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "build-small-hackathon/deku-gguf:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use build-small-hackathon/deku-gguf with Docker Model Runner:
```
docker model run hf.co/build-small-hackathon/deku-gguf:F16
```

Lemonade

How to use build-small-hackathon/deku-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull build-small-hackathon/deku-gguf:F16

Run and chat with the model

lemonade run user.deku-gguf-F16

List all available models

lemonade list

Deku — GGUF (llama.cpp)

GGUF builds of build-small-hackathon/deku, the One for All student: a Qwen2.5-0.5B distilled from 6 teachers via gated CKA geometry distillation. The LoRA adapter is merged into the base, then converted with llama.cpp's convert_hf_to_gguf.py.

Files

File	Size	Use
`deku-q8_0.gguf`	~531 MB	what the Space serves — near-lossless, CPU-friendly
`deku-f16.gguf`	~994 MB	archival full-precision build
`gating.npz`	~22 KB	the teacher-gating head as numpy (`weight` 6×896, `bias` 6)

Run

llama-cli -m deku-q8_0.gguf -p "Explain gradient descent in one sentence."

from llama_cpp import Llama
llm = Llama(model_path="deku-q8_0.gguf", n_ctx=2048)
print(llm.create_chat_completion(
    messages=[{"role": "user", "content": "Why is the sky blue?"}]
)["choices"][0]["message"]["content"])

Teacher gating without torch

gating.npz lets you reproduce the live "teacher influence" meters from the Space using only numpy on a mean-pooled embedding from llama.cpp:

import numpy as np
g = np.load("gating.npz")          # g["weight"] (6, 896), g["bias"] (6,)
def gate(emb):                     # emb: 896-dim pooled embedding
    z = g["weight"] @ emb + g["bias"]
    e = np.exp(z - z.max())
    return e / e.sum()             # softmax over the 6 teachers