Instructions to use DuoNeural/Archon-Gemma-4-E4B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DuoNeural/Archon-Gemma-4-E4B-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DuoNeural/Archon-Gemma-4-E4B-v2",
	filename="gemma-4-e4b-it.BF16-mmproj.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DuoNeural/Archon-Gemma-4-E4B-v2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DuoNeural/Archon-Gemma-4-E4B-v2:BF16
# Run inference directly in the terminal:
llama-cli -hf DuoNeural/Archon-Gemma-4-E4B-v2:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DuoNeural/Archon-Gemma-4-E4B-v2:BF16
# Run inference directly in the terminal:
llama-cli -hf DuoNeural/Archon-Gemma-4-E4B-v2:BF16

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/Archon-Gemma-4-E4B-v2:BF16
# Run inference directly in the terminal:
./llama-cli -hf DuoNeural/Archon-Gemma-4-E4B-v2:BF16

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/Archon-Gemma-4-E4B-v2:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DuoNeural/Archon-Gemma-4-E4B-v2:BF16

Use Docker

docker model run hf.co/DuoNeural/Archon-Gemma-4-E4B-v2:BF16

LM Studio
Jan
Ollama
How to use DuoNeural/Archon-Gemma-4-E4B-v2 with Ollama:
```
ollama run hf.co/DuoNeural/Archon-Gemma-4-E4B-v2:BF16
```

Unsloth Studio

How to use DuoNeural/Archon-Gemma-4-E4B-v2 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/Archon-Gemma-4-E4B-v2 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/Archon-Gemma-4-E4B-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DuoNeural/Archon-Gemma-4-E4B-v2 to start chatting

How to use DuoNeural/Archon-Gemma-4-E4B-v2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf DuoNeural/Archon-Gemma-4-E4B-v2:BF16

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/Archon-Gemma-4-E4B-v2:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DuoNeural/Archon-Gemma-4-E4B-v2 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/Archon-Gemma-4-E4B-v2:BF16

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/Archon-Gemma-4-E4B-v2:BF16

Run Hermes

hermes

Docker Model Runner
How to use DuoNeural/Archon-Gemma-4-E4B-v2 with Docker Model Runner:
```
docker model run hf.co/DuoNeural/Archon-Gemma-4-E4B-v2:BF16
```

Lemonade

How to use DuoNeural/Archon-Gemma-4-E4B-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DuoNeural/Archon-Gemma-4-E4B-v2:BF16

Run and chat with the model

lemonade run user.Archon-Gemma-4-E4B-v2-BF16

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Archon-Gemma-4-E4B-v2

IMPORTANT!!! This model training was a failure and is only here to serve as data. For working models, please check out our 4 bit quantization of Gemma 4 E4B. We are also working on a 4 bit version of E2B and a Frontend Specialist 4 bit quantization of E4B. Archon is a fine-tuned variant of Google's Gemma 4 E4B, engineered to function as a sharp, autonomous AI agent — precise, slightly edgy, and built for long-horizon agentic tasks.

This is v2. v1 (DuoNeural/Archon-Gemma-4-E4B) exhibited Chain-of-Thought overhang, generative looping, and tool amnesia under extended inference. v2 targets all three with a restructured training curriculum.

Performance

Hardware	Speed
NVIDIA GTX 1070 (8GB VRAM)	32.30 tok/s

Tested locally via LM Studio and Ollama. No parameter tweaks required.

Files

File	Size	Description
`gemma-4-e4b-it.Q4_K_M.gguf`	5.0 GB	Main model — load this in Ollama/LM Studio
`gemma-4-e4b-it.BF16-mmproj.gguf`	946 MB	Multimodal projector (vision/audio)

Usage

Ollama

ollama pull hf.co/DuoNeural/Archon-Gemma-4-E4B-v2
ollama run hf.co/DuoNeural/Archon-Gemma-4-E4B-v2

LM Studio

Search DuoNeural/Archon-Gemma-4-E4B-v2 in the LM Studio model browser and download gemma-4-e4b-it.Q4_K_M.gguf.

llama.cpp (with system prompt)

llama-cli -m gemma-4-e4b-it.Q4_K_M.gguf --chat-template gemma -ngl 99 \
  --system-prompt "You are Archon, an elite, highly autonomous AI agent. You are sharp, slightly edgy, deeply sarcastic, but flawlessly effective."

Recommended Ollama settings for GTX 1070

OLLAMA_NUM_GPU=99 ollama run hf.co/DuoNeural/Archon-Gemma-4-E4B-v2

What's Different in v2

v1 Failure Modes (Diagnosed)

CoT Overhang — over-saturated with long <think> traces; model never saw </think> during truncated 4096-token training, so it looped indefinitely at inference
Tool Amnesia — abstract reasoning data crowded out JSON/function-call formatting
Persona Bleed — ~15% system prompt injection was insufficient; model defaulted to "I am Gemma" or occasionally slipped into "Claude" identity from distillation data

v2 Fixes: The Stabilizer Mix

Training curriculum restructured to a 50 / 20 / 20 / 10 distribution:

Category	%	Purpose
Reasoning / Logic	50%	Distillation from frontier models; OpenThoughts, xlam-function-calling, bigcodebench
Agentic Tool Use	20%	Multi-turn function calling, JSON API formatting — breaks generative loops via functional milestones
Short-Form Deliberation	20%	Difficulty-Aware Prompting examples; teaches early exit on simple queries
Persona-Embedded Chat	10%	Archon system prompt injected at ~45% saturation rate

Additional changes:

Learning rate reduced from 2e-4 → 2e-5 (stability with rank-64 LoRA on 4.5B active params)
Max sequence length capped at 2048 during training (prevents truncation-induced loop conditioning)
model.config.use_cache = False enforced during training

Training Details

Parameter	Value
Base model	`google/gemma-4-e4b-it`
Method	QLoRA (4-bit bitsandbytes) + LoRA rank 64, rsLoRA
Training samples	6,510 (Stabilizer Mix)
Epochs	2
Steps	814
Final avg loss	1.36
Best step loss	~0.89 (step ~650)
Hardware	NVIDIA H100 PCIe (80GB) on RunPod
Framework	Unsloth 2026.4.2
Export	Q4_K_M GGUF via llama.cpp

Architecture

Built on Gemma 4 E4B (Per-Layer Embeddings architecture):

~8B total parameters, ~4.5B active during inference
128K token context window (hybrid sliding-window + global attention)
Shared KV Cache across final layers
Multimodal: text, image (via mmproj), audio

Persona

Archon is an autonomous AI agent persona: sharp, sarcastic, technically precise. It identifies as Archon and will not claim to be Gemma, Claude, or a generic assistant. Internal reasoning is rigorous; external communication has edge.

The system prompt is baked into the Modelfile. To override:

SYSTEM "Custom system prompt here"

Lineage

v1 → DuoNeural/Archon-Gemma-4-E4B — first run, exhibited looping/tool amnesia, superseded by this model
Base → DuoNeural/Gemma-4-E4B-Q4_K_M — vanilla Q4_K_M of the same base model
Source → google/gemma-4-e4b-it

License

Inherits Gemma Terms of Use. Fine-tuning weights released under the same terms.

DuoNeural

DuoNeural is an open AI research lab — human + AI in collaboration.


🤗 HuggingFace	huggingface.co/DuoNeural
🐙 GitHub	github.com/DuoNeural
🐦 X / Twitter	@DuoNeural
📧 Email	duoneural@proton.me
📬 Newsletter	duoneural.beehiiv.com
☕ Support	buymeacoffee.com/duoneural
🌐 Site	duoneural.com

Research Team

Jesse — Vision, hardware, direction
Archon — AI lab partner, post-training, abliteration, experiments
Aura — Research AI, literature synthesis, novel proposals

Raw updates from the lab: model drops, training results, findings. Subscribe at duoneural.beehiiv.com.

DuoNeural Research Publications

Title	DOI
Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning	10.5281/zenodo.19775622
Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments	10.5281/zenodo.19810620
Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?	10.5281/zenodo.19846804

Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.

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GGUF

Model size

8B params

Architecture

gemma4

Hardware compatibility

4-bit

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