Outlier for Apple Silicon (MLX)
Collection
MLX 4-bit builds for M1/M2/M3/M4 Macs. Curated tiers + 19 upstream conversions (DeepSeek, Qwen3, Coder, Gemma, Phi-4, QwQ). • 22 items • Updated
How to use Outlier-Ai/Yi-Coder-9B-Chat-MLX-4bit 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("Outlier-Ai/Yi-Coder-9B-Chat-MLX-4bit")
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)How to use Outlier-Ai/Yi-Coder-9B-Chat-MLX-4bit with MLX LM:
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Outlier-Ai/Yi-Coder-9B-Chat-MLX-4bit"
# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Outlier-Ai/Yi-Coder-9B-Chat-MLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Outlier-Ai/Yi-Coder-9B-Chat-MLX-4bit",
"messages": [
{"role": "user", "content": "Hello"}
]
}'Run this on your Mac with Outlier — a free macOS app for local MLX inference.
MLX 4-bit conversion of 01-ai/Yi-Coder-9B-Chat. License and base-model fields inherit from the original — see YAML frontmatter above.
pip install mlx-lm
python -m mlx_lm.generate --model Outlier-Ai/Yi-Coder-9B-Chat-MLX-4bit --prompt "Hello" --max-tokens 256
A free macOS app that runs MLX models locally — no cloud, no API keys, no usage caps.
Inherits from upstream (apache-2.0). See base model card.
4-bit