MLX
Safetensors
gemma4
Merge
mergekit
reasoning
non-reasoning
creative writing
roleplay
conversational
uncensored
31B
gemma-4
mlx-my-repo
8-bit precision
Instructions to use ailexleon/Versipellis-31B-mlx-8Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ailexleon/Versipellis-31B-mlx-8Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Versipellis-31B-mlx-8Bit ailexleon/Versipellis-31B-mlx-8Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use ailexleon/Versipellis-31B-mlx-8Bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "ailexleon/Versipellis-31B-mlx-8Bit"
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": "ailexleon/Versipellis-31B-mlx-8Bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use ailexleon/Versipellis-31B-mlx-8Bit 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 "ailexleon/Versipellis-31B-mlx-8Bit"
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 ailexleon/Versipellis-31B-mlx-8Bit
Run Hermes
hermes
ailexleon/Versipellis-31B-mlx-8Bit
The Model ailexleon/Versipellis-31B-mlx-8Bit was converted to MLX format from Nimbz/Versipellis-31B using mlx-lm version 0.31.2.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("ailexleon/Versipellis-31B-mlx-8Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
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Model size
31B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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8-bit
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Base model
Nimbz/Versipellis-31B