Instructions to use Subject-Emu-5259/NeuralAI-Mamba-K1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use Subject-Emu-5259/NeuralAI-Mamba-K1 with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Subject-Emu-5259/NeuralAI-Mamba-K1 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 Subject-Emu-5259/NeuralAI-Mamba-K1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Subject-Emu-5259/NeuralAI-Mamba-K1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Subject-Emu-5259/NeuralAI-Mamba-K1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Subject-Emu-5259/NeuralAI-Mamba-K1: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 Subject-Emu-5259/NeuralAI-Mamba-K1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Subject-Emu-5259/NeuralAI-Mamba-K1: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 Subject-Emu-5259/NeuralAI-Mamba-K1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Subject-Emu-5259/NeuralAI-Mamba-K1:Q4_K_M

Use Docker

docker model run hf.co/Subject-Emu-5259/NeuralAI-Mamba-K1:Q4_K_M

LM Studio
Jan

vLLM

How to use Subject-Emu-5259/NeuralAI-Mamba-K1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Subject-Emu-5259/NeuralAI-Mamba-K1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Subject-Emu-5259/NeuralAI-Mamba-K1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Subject-Emu-5259/NeuralAI-Mamba-K1:Q4_K_M

Ollama
How to use Subject-Emu-5259/NeuralAI-Mamba-K1 with Ollama:
```
ollama run hf.co/Subject-Emu-5259/NeuralAI-Mamba-K1:Q4_K_M
```

Unsloth Studio

How to use Subject-Emu-5259/NeuralAI-Mamba-K1 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 Subject-Emu-5259/NeuralAI-Mamba-K1 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 Subject-Emu-5259/NeuralAI-Mamba-K1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Subject-Emu-5259/NeuralAI-Mamba-K1 to start chatting

Atomic Chat new
Docker Model Runner
How to use Subject-Emu-5259/NeuralAI-Mamba-K1 with Docker Model Runner:
```
docker model run hf.co/Subject-Emu-5259/NeuralAI-Mamba-K1:Q4_K_M
```

Lemonade

How to use Subject-Emu-5259/NeuralAI-Mamba-K1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Subject-Emu-5259/NeuralAI-Mamba-K1:Q4_K_M

Run and chat with the model

lemonade run user.NeuralAI-Mamba-K1-Q4_K_M

List all available models

lemonade list

🧠 NeuralAI: The Generative AI Engine

NeuralAI — Your AI. On your hardware. In your browser.

📊 Repository Composition

Language	Percentage
Python	71.1%
HTML	13.0%
JavaScript	12.4%
CSS	2.6%
Shell	0.4%
Jupyter Notebook	0.3%
Jinja	0.2%

The High-Velocity AI Engine for Your Entire Vibe Stack

NeuralAI is the central intelligence engine developed by De'Andrew Preston Harris. Conceived and engineered as an owned AI platform, it spans fine-tuned transformer models, custom SSM base models, DPO alignment, and a production web UI — all designed for local-first, private AI computing.

🏗️ Model Family

graph TB
    subgraph "NeuralAI Model Family"
        direction TB

        K1["🧬 Mamba K1<br/>130M · SFT 50 steps<br/>First Owned Base"]
        K2["🧬 Mamba K2<br/>793M · Q4_K_M GGUF<br/>460MB · Production"]
        K3["🔬 Mamba K3<br/>SFT 500-1000 steps<br/>10K+ UltraChat<br/>In Training"]

        K1 --> K2 --> K3
        K2 --> PROD["🚀 Active Inference<br/>LM Studio · 460MB RAM<br/>neuralai-web-ui.zocomputer.io"]
    end

    style K1 fill:#4a90d9,color:#fff
    style K2 fill:#22c55e,color:#fff
    style K3 fill:#f59e0b,color:#000
    style PROD fill:#10b981,color:#fff

Complete Lineup

Model	Architecture	Params	Training	Status	Location
Mamba K1	Mamba SSM	130M	SFT LoRA v2 merged (500 steps, 1K UltraChat, intel format) + Q4_K_M GGUF	✅ Published HF + SFT v3 running	`Subject-Emu-5259/NeuralAI-Mamba-K1`
Mamba K2	Mamba SSM	793M	Base pretrained — SFT queued (Q4_K_M GGUF)	⚠️ Base model only	`Subject-Emu-5259/NeuralAI-Mamba-K2`
Mamba K3	Mamba SSM	2.8B	Base pretrained — SFT queued	⚠️ Base model only	local `models/mamba-k3-base/`

Why Mamba SSM

Property	Benefit
Complexity	(O(n)) linear — scales to long context efficiently
Inference	Fast at any sequence length, not just short prompts
Memory	K2 runs at 460MB (Q4_K_M GGUF) — fits on any device
Ownership	NeuralAI trains and merges all release weights on top of public Mamba SSM bases — every released GGUF is a fully merged model, not a raw base
Ecosystem	LM Studio, llama.cpp, Hugging Face — mature deployment options

🌟 Vision & Manifesto

NeuralAI doesn't just predict text; it operates the work. The core mission is to create a multimodal generative system that bridges the gap between raw idea and execution. By fusing autoregressive generation with adaptive agency, NeuralAI becomes more than a chatbot — it is a persistent, reasoning partner.

Born from resilience and ambition in Memphis, Tennessee and West Memphis, Arkansas, NeuralAI represents a forward-thinking approach to personal, private AI computing.

🛠️ Tech Stack & Architecture

NeuralAI is built on a high-performance architecture that decouples the inference engine from the web interface, enabling lightweight cloud hosting with powerful local inference.

Core Stack

Production Model: Mamba K2 793M Q4_K_M GGUF (460MB) via llama.cpp — active inference engine
Model Family: Mamba K1 (130M, SFT LoRA retry) → Mamba K2 (793M base GGUF) → Mamba K3 (2.8B base)
Inference Engine: llama.cpp server with a custom neuralai-intel chat format (vocabulary-friendly for GPT-NeoX / Mamba tokenizers)
Vocal Identity: Andrew (Warm/Multilingual) — Optional voice synthesis integration
Web Interface: Custom Flask UI served via Zo Computer at neuralai-web-ui-deandrewharris.zocomputer.io
Tool Chain: 10 live slash commands (/web, /fetch, /browse, /research, /img, /speak, /summarize, /translate, /news, /yt) + NL→Tool Router

Future Scale Path

Stage	Params	Goal	Status
Mamba K1 SFT v2	130M	Merge + Q4_K_M/F16 GGUF + publish to HF	✅ Done
Mamba K1 SFT v3	130M	1000-step continuation on 10K UltraChat	🔄 Active
Mamba K2 SFT	793M	SFT LoRA 500 steps → merge → Q4_K_M GGUF	📋 Next
Mamba K3 SFT	2.8B	SFT LoRA 1000 steps → merge → Q4_K_M GGUF	📋 Next
Mamba 2B/3B	~2-3B	Scaled SSM architecture, benchmarks	📋 Planned

✨ Key Features & Capabilities

💬 Multimodal Chat & Agentic Intelligence

High-Velocity Text Inference: Fast, local inference with deep context awareness
Deep Reasoning Mode: Integration of test-time compute and chain-of-thought reasoning
Autonomous Agentic Workflows: Agent-mode interaction with browser, terminal, and third-party apps
Live S2S (Speech-to-Speech): Real-time voice interaction with integrated microphone interface
Identity Vault & Memory: Persistent user memory and rule constraints

🔧 Developer & Engineering Tools

10 Web Tool Commands: Search, fetch, browse, research, image gen, TTS, summarize, translate, news, YouTube
NL→Tool Router: Natural language web requests auto-routed to the right tool
Model Manager: CLI switching between all registered models
Benchmark Suite: Perplexity, generation diversity, MMLU-style, reasoning tests

🚀 Model Lineage

timeline
    title NeuralAI Model Evolution
           : Custom 135M base SFT
    2026 Q3 : Mamba K1 — First owned base
           : 130M SSM · Proof of Concept
    2026 Q3 : Mamba K2 — Scaled base
           : 790M Q4_K_M · GGUF ready
    2026 Q3 : Mamba K3 — Full SFT
           : 500-1000 steps · 10K+ samples
    2026 Q4 : Mamba 2B/3B — Next scale targets
           : 2B SSM → 3B Core Intelligence

🚀 Deployment

# 1. Start the inference service
cd NeuralAI
supervisorctl -c /etc/zo/supervisord-user.conf restart neuralai-lmstudio

# 2. Start the web UI service
python3 services/webui_service.py

Mamba K2 (LM Studio / llama.cpp)

# Download from HuggingFace
huggingface-cli download Subject-Emu-5259/NeuralAI-Mamba-K2 \
  mamba-790m-hf.Q4_K_M.gguf --local-dir ./models/

# Serve with the NeuralAI chat format
python3 services/lmstudio_server.py \
  --model models/mamba-790m-hf.Q4_K_M.gguf \
  --chat_format neuralai-intel \
  --port 1234

Mamba K1 (Python)

from transformers import MambaForCausalLM, AutoTokenizer

model = MambaForCausalLM.from_pretrained("Subject-Emu-5259/NeuralAI-Mamba-K1")
tokenizer = AutoTokenizer.from_pretrained("Subject-Emu-5259/NeuralAI-Mamba-K1")

Containerized Deployments

Deployment	Dockerfile	Stack	Status
Web Chat	`services/start_lmstudio.sh` + Flask UI	llama.cpp + `neuralai-web-ui.zocomputer.io`	✅ Live

🌌 NeuralAI Ecosystem

The standalone software implementation of the NeuralAI core is NeuralLabs: 👉 github.com/Subject-Emu-5259/NeuralLabs

Software Downloads: Latest beta builds available at: 👉 zo.pub/deandrewharris/neurallabs-beta

📈 Current State & Active Goals

Legacy DPO v17 / Air 135M / SmolLM2-360M: Retired and removed from the repository
Mamba K1: SFT LoRA v2 merged and published to Hugging Face; a longer v3 SFT run is in progress to reduce repetition
Mamba K2: Base pretrained GGUF ready — awaiting SFT
Mamba K3: 2.8B base downloaded — awaiting SFT
Last Maintenance: August 1, 2026 (Mamba Era — K1 SFT v2 merge, Q4_K_M GGUF, HF publish + v3 SFT started)

👤 Creator

Built by De'Andrew Preston Harris (@deandrewharris94) with Google Gemini AI Studio/Colab collaboration.

From Memphis, Tennessee. Raised in West Memphis, Arkansas. AI Software Engineering at Maestro College.

NeuralAI → Hugging Face sync is live

Downloads last month: 18

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for Subject-Emu-5259/NeuralAI-Mamba-K1

Base model

state-spaces/mamba-130m-hf

Adapter

(27)

this model