Text Generation
MLX
Safetensors
German
English
gemma4
gemma-4
dora
finetuned
4bit
apple-silicon
ailey
conversational
4-bit precision
Instructions to use CptShaggy/AileyNitro-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use CptShaggy/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("CptShaggy/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
- Pi
How to use CptShaggy/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 "CptShaggy/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": "CptShaggy/AileyNitro-2B" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use CptShaggy/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 "CptShaggy/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 CptShaggy/AileyNitro-2B
Run Hermes
hermes
- MLX LM
How to use CptShaggy/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 "CptShaggy/AileyNitro-2B"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "CptShaggy/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": "CptShaggy/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
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Model size
1B params
Tensor type
BF16
·
U32 ·
F32 ·
Hardware compatibility
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4-bit