Instructions to use Irfanuruchi/Phi-4-mini-instruct-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Irfanuruchi/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("Irfanuruchi/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

How to use Irfanuruchi/Phi-4-mini-instruct-MLX-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Irfanuruchi/Phi-4-mini-instruct-MLX-4bit"

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": "Irfanuruchi/Phi-4-mini-instruct-MLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Irfanuruchi/Phi-4-mini-instruct-MLX-4bit 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 "Irfanuruchi/Phi-4-mini-instruct-MLX-4bit"

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 Irfanuruchi/Phi-4-mini-instruct-MLX-4bit

Run Hermes

hermes

MLX LM

How to use Irfanuruchi/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 "Irfanuruchi/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 "Irfanuruchi/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": "Irfanuruchi/Phi-4-mini-instruct-MLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Phi-4-mini-instruct (MLX 4-bit)

This is a 4-bit MLX quantized version of microsoft/Phi-4-mini-instruct, optimized for Apple Silicon and local / on-device inference.

Benchmark Environment

Device: MacBook Pro (M3 Pro)
Runtime: MLX
Precision: 4-bit (~4.5 bits per weight)

Performance (Measured)

Disk size: ~2.0 GB
Peak memory: ~2.24 GB
Generation speed: ~56 tokens/sec

Benchmarks were collected on macOS (M3 Pro).
iPhone / iPad performance will vary depending on hardware and memory.

Usage

mlx_lm.generate \
  --model Irfanuruchi/Phi-4-mini-instruct-MLX-4bit \
  --prompt "Give me 5 short offline assistant tips." \
  --max-tokens 120

License

Original model license applies. See microsoft/Phi-4-mini-instruct.

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Safetensors

Model size

0.6B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for Irfanuruchi/Phi-4-mini-instruct-MLX-4bit

Base model

microsoft/Phi-4-mini-instruct

Quantized

(149)

this model