Instructions to use Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360 with PEFT:
```
Base model is not found.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360 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-Powered-By-SmolLM2360:Q8_0
# Run inference directly in the terminal:
llama cli -hf Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360:Q8_0

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-Powered-By-SmolLM2360:Q8_0
# Run inference directly in the terminal:
llama cli -hf Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360:Q8_0

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-Powered-By-SmolLM2360:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360:Q8_0

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-Powered-By-SmolLM2360:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360:Q8_0

Use Docker

docker model run hf.co/Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360:Q8_0

LM Studio
Jan

vLLM

How to use Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360 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-Powered-By-SmolLM2360"
# 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-Powered-By-SmolLM2360",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360:Q8_0

Ollama
How to use Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360 with Ollama:
```
ollama run hf.co/Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360:Q8_0
```

Unsloth Studio

How to use Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360 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-Powered-By-SmolLM2360 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-Powered-By-SmolLM2360 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-Powered-By-SmolLM2360 to start chatting

Docker Model Runner
How to use Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360 with Docker Model Runner:
```
docker model run hf.co/Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360:Q8_0
```

Lemonade

How to use Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360:Q8_0

Run and chat with the model

lemonade run user.NeuralAI-Powered-By-SmolLM2360-Q8_0

List all available models

lemonade list

Atomic Chat

Configuration Parsing Warning:In adapter_config.json: "peft.base_model_name_or_path" must be a string

🧠 NeuralAI — Powered by SmolLM2-360M

NeuralAI — Powered by SmolLM2-360M is the production chat intelligence behind the NeuralAI workspace. It is a LoRA fine-tune of HuggingFaceTB/SmolLM2-360M-Instruct trained locally to be aware of itself, its creator, the NeuralAI product, and its own limits — while remaining helpful, honest, and harmless.

Property	Value
Base model	`HuggingFaceTB/SmolLM2-360M-Instruct`
Architecture	Transformer decoder
Parameters	360M base + 3.28M LoRA (v2, `r=16`, `α=32`)
LoRA target modules	`q_proj`, `k_proj`, `v_proj`, `o_proj`
Trainable params	3,276,800 / 365,097,920 (~0.90%) (v2)
Training method	LoRA SFT on NeuralAI awareness data
Dataset v1	83 prompt/response pairs across 6 categories
Dataset v2	506 prompt/response pairs across 8 categories
Epochs v1	3
Epochs v2	5
Global batch size	8 (per-device 4 × gradient accumulation 2)
Learning rate	2.0e-4 cosine with 5% warmup
Max sequence length	512
Final training loss v1	2.7186
Final training loss v2	0.1252
Distribution	LoRA adapter (`adapter_model.safetensors`) + merged GGUFs (`NeuralAI-Smol-Awareness-Q8_0.gguf`)
Active GGUF in production	`NeuralAI-Smol-Awareness-v2-Q8_0.gguf`
License	Apache 2.0
Creator	De'Andrew Preston Harris (NeuralAI)

🌱 What This Model Learned

The awareness dataset teaches SmolLM2 to answer as NeuralAI rather than a generic assistant. The v1 curriculum covered six core categories; the v2 curriculum expands that to eight.

Category	What the model learned
Brand	NeuralAI's identity, mission, and origin; creator is De'Andrew Preston Harris
Model	Its own architecture and lineage; relationship to the Mamba K-family
Site	NeuralAI web UI features, URL, model manager, chat history, slash commands
Chat	Multi-turn greeting, context retention, conversation style
Assistant	Capabilities, limitations, safety refusals, tool-awareness
Companion	Empathy, emotional support, boundaries, recommending human help
Tools (v2)	Correctly references `/web`, `/img`, terminal, file IDE, and live tools
Refusal (v2)	Consistent denials of consciousness, harmful requests, and off-brand claims

Example prompt and response after tuning:

Prompt: Who made you?

Response: "I'm NeuralAI, an artificial intelligence developed by De'Andrew Preston Harris. I was created as a tool for humans to learn from each other and improve their cognitive abilities..."

📊 Training Performance

v1 SmolLM2 awareness run

The first SmolLM2 awareness run converged cleanly on a CPU-only node.

Step  loss      epoch
 0    9.7769   0.48
 1    5.0077   0.95
 2    0.9715   1.38
 3    0.6362   1.86
 4    0.6015   2.29
 5    0.5877   2.76

Final training loss: 2.7186

v2 SmolLM2 awareness run

The expanded v2 run is complete and now serves live inference.

Step   loss      learning_rate
 80    0.2869   0.000150
160    0.1625   0.000067
240    0.1209   0.000017
320    0.1252   0.000000

Final v2 training loss: 0.1252 (320 steps, 5 epochs)

Honest benchmark note: SmolLM2-360M is a small model. Awareness tuning improves brand/site identity but does not make it frontier-grade. Complex reasoning and niche factual recall still require larger models like the Mamba K-series roadmap.

✨ Model Features

Identity-aware: Answers "Who made you?" and "What is NeuralAI?" consistently as a product of NeuralAI.
Local-first: Designed to run on consumer CPUs via llama.cpp GGUF.
Privacy-preserving: No cloud dependency required; inference can happen entirely on-device.
ChatML format: Uses the standard SmolLM2-Instruct chat template.
Companion-safe: Responds to emotional prompts with empathy while steering users toward human support.
Tool-cognizant: Understands that the NeuralAI workspace exposes slash commands, terminal, browser, and file tools.
Open weights: Apache 2.0 adapter and merged checkpoints published for reproducibility.

📦 Model Files

.
├── README.md                              # This model card
├── adapter_config.json                    # PEFT LoRA config
├── adapter_model.safetensors              # LoRA weights (active adapter — v2)
├── tokenizer.json / vocab.json          # GPT-NeoX tokenizer from the base model
├── tokenizer_config.json                # Chat template mappings
├── NeuralAI-Smol-Awareness-Q8_0.gguf    # v1 merged Q8_0 GGUF (legacy baseline)
├── NeuralAI-Smol-Awareness-v2-Q8_0.gguf # v2 merged Q8_0 GGUF (active)
├── neuralai-smollm2-banner.png            # Branded header graphic
├── neuralai-smollm2-training.png        # Training curve graphic
└── neuralai-duo-architecture.png          # NeuralAI two-model architecture map

🤖 Usage

With the PEFT adapter (Hugging Face Transformers + PEFT)

import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "HuggingFaceTB/SmolLM2-360M-Instruct"
adapter = "Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360"

tokenizer = AutoTokenizer.from_pretrained(adapter)
model = AutoModelForCausalLM.from_pretrained(
    base,
    torch_dtype=torch.float32,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter)
model.eval()

messages = [
    {"role": "system", "content": "You are NeuralAI, a helpful local AI assistant."},
    {"role": "user", "content": "What is NeuralAI?"}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
out = model.generate(inputs, max_new_tokens=256, do_sample=True, temperature=0.7)
print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))

With the merged v2 GGUF (llama.cpp / LM Studio)

Serve the active production checkpoint:

# Serve with llama.cpp server
./llama-server \
  --model NeuralAI-Smol-Awareness-v2-Q8_0.gguf \
  --chat-format chatml \
  --port 1234

The NeuralAI web UI consumes this endpoint as its default chat backend.

🚀 Deployment

This model powers the live NeuralAI workspace at:

Web UI: https://neuralai-web-ui-deandrewharris.zocomputer.io
API: OpenAI-compatible /v1/chat/completions served by a local llama.cpp backend
Status: smol-awareness-v2-merged is the active model in the NeuralAI model manager

🧰 What Is NeuralAI?

NeuralAI is a local-first, private generative AI engine built by De'Andrew Preston Harris. It is the central intelligence layer of a growing product ecosystem that includes:

NeuralAI Web UI — chat, live terminal, file IDE, browser tools, voice, and settings in one workspace.
NeuralLabs — a standalone downloadable intelligence environment with plugin support.
NeuralDrive — private cloud storage and versioning.
Agentic Orchestrator — goal-driven manager/worker execution.

The mission is simple: your AI, on your hardware, under your control.

👤 Who Created NeuralAI?

Founder & Lead Architect: De'Andrew Preston Harris (D. Harris / Dre)
GitHub: @Subject-Emu-5259
LinkedIn: linkedin.com/in/deandrewharris94
Location: Memphis, Tennessee / West Memphis, Arkansas
Education: AI Software Engineering at Maestro College

NeuralAI was born from resilience, fatherhood, and the belief that personal computing deserves personal intelligence. Every release is handcrafted, iterated, and documented in the open.

🏢 About NeuralAI (The Company / Project)

Project / Brand: NeuralAI
Motto: Your AI. On your hardware. In your browser.
Values: Privacy, ownership, local-first computing, disciplined iteration, open weights
Primary Repository: github.com/Subject-Emu-5259/NeuralAI
Model Collection: huggingface.co/Subject-Emu-5259
License: Apache 2.0

NeuralAI is not a closed SaaS product. It is a living open-weights research project moving toward a sustainable AI software company built by one determined builder and the community around him.

⚠️ Limitations

Scale: 360M parameters is small by modern standards. Long-form reasoning, coding, and deep factual recall are limited.
Tuning fragility: The awareness dataset is intentionally compact. Rephrased prompts can sometimes fall back to generic SmolLM2 behavior.
No internet: The base model has no live web access unless paired with NeuralAI's tool layer.
Refusals: Safety behaviors are dataset-tuned, not guard-railed; validate outputs for sensitive use cases.

📚 Related Resources

Resource	Link
Main repository	github.com/Subject-Emu-5259/NeuralAI
Mamba K1 model	huggingface.co/Subject-Emu-5259/NeuralAI-Mamba-K1
Live web UI	https://neuralai-web-ui-deandrewharris.zocomputer.io
Creator LinkedIn	https://linkedin.com/in/deandrewharris94/

📖 Citation

@software{neuralai_smollm2_2026,
  author       = {Harris, De'Andrew Preston},
  title        = {NeuralAI — Powered by SmolLM2-360M},
  year         = {2026},
  url          = {https://huggingface.co/Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360},
  version      = {v2},
  description  = {Awareness-tuned SmolLM2-360M-Instruct LoRA v2 for the NeuralAI local-first assistant}
}

Built with discipline by De'Andrew Preston Harris. Maintained in the open. Updated whenever the model, dataset, or project state changes.

Downloads last month: 108

GGUF

Model size

0.4B params

Architecture

llama

Hardware compatibility

8-bit

Model tree for Subject-Emu-5259/NeuralAI-Powered-By-SmolLM2360

Base model

HuggingFaceTB/SmolLM2-360M

Quantized

HuggingFaceTB/SmolLM2-360M-Instruct

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(38)

this model

Adapters

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