Text Generation
MLX
Safetensors
phi3
4-bit precision
4bit
apple-silicon
chat
conversational
edge-ai
efficient
fast
function-calling
instruct
local-llm
m1
m2
m3
m4
mac
mac-mini
mac-studio
macbook-air
macbook-pro
macos
metal
microsoft
mlx-community
mlx-lm
no-cloud
offline
on-device
outlier
outlier-app
phi
phi-4
phi4
private
private-ai
quantized
small-llm
tool-use
custom_code
Instructions to use Outlier-Ai/Phi-4-mini-instruct-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Outlier-Ai/Phi-4-mini-instruct-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/Phi-4-mini-instruct-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) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use Outlier-Ai/Phi-4-mini-instruct-MLX-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "Outlier-Ai/Phi-4-mini-instruct-MLX-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "Outlier-Ai/Phi-4-mini-instruct-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/Phi-4-mini-instruct-MLX-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
- Xet hash:
- c80012e0e46ff4983046e9e395fb7eb4e36f9daeb63e369114724043247c7a42
- Size of remote file:
- 2.16 GB
- SHA256:
- d1d56b7fe2afdca940db827b0b445be6ae46f637ec9976d98b29896064d4e5bd
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