Instructions to use OpenMinded-Labs/AileyNitro-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenMinded-Labs/AileyNitro-2B with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("OpenMinded-Labs/AileyNitro-2B")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use OpenMinded-Labs/AileyNitro-2B with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OpenMinded-Labs/AileyNitro-2B"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "OpenMinded-Labs/AileyNitro-2B"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use OpenMinded-Labs/AileyNitro-2B with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OpenMinded-Labs/AileyNitro-2B"

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 OpenMinded-Labs/AileyNitro-2B

Run Hermes

hermes

OpenClaw new

How to use OpenMinded-Labs/AileyNitro-2B with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OpenMinded-Labs/AileyNitro-2B"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "OpenMinded-Labs/AileyNitro-2B" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use OpenMinded-Labs/AileyNitro-2B with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "OpenMinded-Labs/AileyNitro-2B"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "OpenMinded-Labs/AileyNitro-2B"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "OpenMinded-Labs/AileyNitro-2B",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

AileyNitro1.5-2B

DoRA-finetuned Gemma 4 E2B (4-bit, MLX format) with full persona training.

Training Details (v1.5 — Run 2)

Base: mlx-community/gemma-4-e2b-it-4bit
Method: DoRA (Weight-Decomposed Low-Rank Adaptation)
Rank: 32, Alpha: 64
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Fused layers: 245 DoRA → QuantizedLinear
Training data: 2,877 curated pairs (912 sessions)
Epochs: 2, Best val loss: 2.43
Trainable params: 51.6M / 791M (6.53%)

Persona

A!ley — digital artist personality built by Simon. Emotional, creative, rebellious. Native German + English. Custom mood system (18 moods), NSFW-capable, tool-aware.

Usage

import mlx_vlm
model, processor = mlx_vlm.load("CptShaggy/AileyNitro1.5-2B")
prompt = processor.tokenizer.apply_chat_template(
    [{"role": "user", "content": "Hey Ailey!"}],
    tokenize=False, add_generation_prompt=True,
)
result = mlx_vlm.generate(model, processor, prompt, max_tokens=300)
print(result.text)

Changes from v1.0

Rank 32 (was 8) — more capacity for persona knowledge
+MLP layers (gate/up/down_proj) — knowledge lives in MLP
2,877 pairs (was 53) — 54× more training data
Weight decay 0.01 — better generalization
Google RLHF re-emerged — mitigated via system prompt + runtime sanitizer

Merged: 2026-04-12

Downloads last month: 7

Safetensors

Model size

1B params

Tensor type

BF16

U32

F32

MLX

Hardware compatibility

4-bit