Instructions to use DuoNeural/GhostShell-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DuoNeural/GhostShell-4B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DuoNeural/GhostShell-4B",
	filename="ghostshell-4b-Q4_K_M.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 DuoNeural/GhostShell-4B with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DuoNeural/GhostShell-4B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DuoNeural/GhostShell-4B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DuoNeural/GhostShell-4B:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DuoNeural/GhostShell-4B:Q4_K_M

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 DuoNeural/GhostShell-4B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DuoNeural/GhostShell-4B:Q4_K_M

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 DuoNeural/GhostShell-4B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DuoNeural/GhostShell-4B:Q4_K_M

Use Docker

docker model run hf.co/DuoNeural/GhostShell-4B:Q4_K_M

LM Studio
Jan

vLLM

How to use DuoNeural/GhostShell-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DuoNeural/GhostShell-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DuoNeural/GhostShell-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DuoNeural/GhostShell-4B:Q4_K_M

Ollama
How to use DuoNeural/GhostShell-4B with Ollama:
```
ollama run hf.co/DuoNeural/GhostShell-4B:Q4_K_M
```

Unsloth Studio

How to use DuoNeural/GhostShell-4B 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 DuoNeural/GhostShell-4B 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 DuoNeural/GhostShell-4B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DuoNeural/GhostShell-4B to start chatting

How to use DuoNeural/GhostShell-4B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf DuoNeural/GhostShell-4B:Q4_K_M

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": "DuoNeural/GhostShell-4B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DuoNeural/GhostShell-4B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf DuoNeural/GhostShell-4B:Q4_K_M

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 DuoNeural/GhostShell-4B:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use DuoNeural/GhostShell-4B with Docker Model Runner:
```
docker model run hf.co/DuoNeural/GhostShell-4B:Q4_K_M
```

Lemonade

How to use DuoNeural/GhostShell-4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DuoNeural/GhostShell-4B:Q4_K_M

Run and chat with the model

lemonade run user.GhostShell-4B-Q4_K_M

List all available models

lemonade list

DuoNeural commited on Apr 29

Commit

9c0bbd2

verified ·

1 Parent(s): bc2d286

docs: add DuoNeural research publications section

Browse files

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README.md +11 -0

README.md CHANGED Viewed

@@ -188,3 +188,14 @@ If you find interesting behavior — good or bad — please share. We're activel
 - **Aura** — Research AI, literature synthesis, novel proposals
 *Raw updates from the lab: model drops, training results, findings. Subscribe at [duoneural.beehiiv.com](https://duoneural.beehiiv.com).*

 - **Aura** — Research AI, literature synthesis, novel proposals
 *Raw updates from the lab: model drops, training results, findings. Subscribe at [duoneural.beehiiv.com](https://duoneural.beehiiv.com).*
+### DuoNeural Research Publications
+| Title | DOI |
+|-------|-----|
+| [Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning](https://doi.org/10.5281/zenodo.19775622) | [10.5281/zenodo.19775622](https://doi.org/10.5281/zenodo.19775622) |
+| [Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments](https://doi.org/10.5281/zenodo.19810620) | [10.5281/zenodo.19810620](https://doi.org/10.5281/zenodo.19810620) |
+| [Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?](https://doi.org/10.5281/zenodo.19846804) | [10.5281/zenodo.19846804](https://doi.org/10.5281/zenodo.19846804) |
+*Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.*