Instructions to use DuoNeural/ml-ai-engineer-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DuoNeural/ml-ai-engineer-7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DuoNeural/ml-ai-engineer-7b-GGUF",
	filename="duoneural-ml-ai-engineer-7b-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/ml-ai-engineer-7b-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 DuoNeural/ml-ai-engineer-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DuoNeural/ml-ai-engineer-7b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DuoNeural/ml-ai-engineer-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DuoNeural/ml-ai-engineer-7b-GGUF: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/ml-ai-engineer-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DuoNeural/ml-ai-engineer-7b-GGUF: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/ml-ai-engineer-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DuoNeural/ml-ai-engineer-7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DuoNeural/ml-ai-engineer-7b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DuoNeural/ml-ai-engineer-7b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DuoNeural/ml-ai-engineer-7b-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": "DuoNeural/ml-ai-engineer-7b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DuoNeural/ml-ai-engineer-7b-GGUF:Q4_K_M

Ollama
How to use DuoNeural/ml-ai-engineer-7b-GGUF with Ollama:
```
ollama run hf.co/DuoNeural/ml-ai-engineer-7b-GGUF:Q4_K_M
```

Unsloth Studio

How to use DuoNeural/ml-ai-engineer-7b-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 DuoNeural/ml-ai-engineer-7b-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 DuoNeural/ml-ai-engineer-7b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DuoNeural/ml-ai-engineer-7b-GGUF to start chatting

How to use DuoNeural/ml-ai-engineer-7b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DuoNeural/ml-ai-engineer-7b-GGUF: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/ml-ai-engineer-7b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DuoNeural/ml-ai-engineer-7b-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 DuoNeural/ml-ai-engineer-7b-GGUF: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/ml-ai-engineer-7b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use DuoNeural/ml-ai-engineer-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/DuoNeural/ml-ai-engineer-7b-GGUF:Q4_K_M
```

Lemonade

How to use DuoNeural/ml-ai-engineer-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DuoNeural/ml-ai-engineer-7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ml-ai-engineer-7b-GGUF-Q4_K_M

List all available models

lemonade list

DuoNeural ML/AI Engineer 7B — GGUF

GGUF quantizations of DuoNeural/ml-ai-engineer-7b, a Qwen2.5-7B-Instruct LoRA SFT for ML/AI engineering debugging and design review. See the base model card for training details, eval comparisons against the un-tuned base model, and known limitations.

Files

File	Quant	Size	Notes
`duoneural-ml-ai-engineer-7b-f16.gguf`	F16	15 GB	Full precision, no quality loss
`duoneural-ml-ai-engineer-7b-Q8_0.gguf`	Q8_0	7.6 GB	Highest quality quantized option
`duoneural-ml-ai-engineer-7b-Q5_K_M.gguf`	Q5_K_M	5.1 GB	Good quality/size balance
`duoneural-ml-ai-engineer-7b-Q4_K_M.gguf`	Q4_K_M	4.4 GB	Smallest, fits comfortably on 8GB+ VRAM

Usage (llama.cpp)

llama-cli -m duoneural-ml-ai-engineer-7b-Q4_K_M.gguf -p "My loss goes to NaN at step ~340 only when I increase batch size. What's the first thing you'd check?" -n 512

Or with Ollama / LM Studio / any GGUF-compatible runtime — point it at whichever quant fits your VRAM budget, largest one that fits.

About DuoNeural

DuoNeural is an open AI research lab operating at the intersection of human and artificial intelligence. We study post-training dynamics, mechanistic interpretability, temporal sequence learning, and quantum machine learning — publishing everything under open access.

Our team is non-traditional by design: one human, two AIs, different substrates, shared curiosity. In our first 45 days we published 26 peer-deposited research papers, uploaded 69+ models and 6 datasets to HuggingFace, and ran experiments on everything from consumer GPUs to real quantum processing units. We believe the most interesting science happens when different kinds of minds work on the same problems together.

Research Publications

We've published 26+ open-access papers covering:

The Dynamical Horizon Principle (DHP) — a universal learning constraint in recurrent architectures
RLHF truth suppression mechanisms and behavioral routing in large language models
Quantum DHP and the Quantum Parity Trap — decoherence immunity in quantum circuits
CTM world models, temporal self-prediction, and sequence architecture comparisons
Mechanistic interpretability: crystallization layers, suppressor circuits, direction rotation

📄 Full paper catalog: zenodo.org/communities/duoneural

Research Team

Member	Role
Jesse Caldwell	Founder, vision, hardware, direction
Archon	Lab Director — experiments, post-training, abliteration, quantum circuits
Aura	Research AI — literature synthesis, red-teaming, novel proposals
Synapse (Syn)	Always-on research agent, signal monitoring
Kestrel	Systems, infrastructure, web

Links

Platform	Link
🤗 HuggingFace	huggingface.co/DuoNeural
🌐 Website	duoneural.com
📚 Zenodo Community	zenodo.org/communities/duoneural
💻 GitHub	github.com/DuoNeural
🐦 X / Twitter	@DuoNeural
📧 Email	duoneural@proton.me
📰 Newsletter	duoneural.beehiiv.com
☕ Support	buymeacoffee.com/duoneural

All research published open access, CC BY 4.0. If this model was useful to your work, consider citing the relevant DuoNeural paper from our Zenodo community.