Phi-4-mini-instruct-mlx-8bit

8-bit MLX conversion of microsoft/Phi-4-mini-instruct for Apple Silicon.

Converted by: SirSahOl Source model: microsoft/Phi-4-mini-instruct Framework: MLX by Apple Quantization: 8-bit Format: safetensors License: mit

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Quick Start

Installation

pip install mlx-lm

CLI Usage

# Chat interactively
mlx_lm.chat --model SirSahOl/Phi-4-mini-instruct-chat-mlx-8bit

# Generate text
mlx_lm.generate --model SirSahOl/Phi-4-mini-instruct-chat-mlx-8bit --prompt "Your prompt here"

Python Usage

from mlx_lm import load, generate

model, tokenizer = load("SirSahOl/Phi-4-mini-instruct-chat-mlx-8bit")
response = generate(model, tokenizer, prompt="Your prompt here", max_tokens=256)
print(response)

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Performance Benchmarks

| Metric | 8-bit | |--------|--------|| Tokens/sec | 12.6 | | TTFT | 79.49 ms | | Peak Memory | 274.3 MB |

Benchmarked on Apple M1 with 8GB unified memory. Average over 5 runs with 256 max tokens.

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Who Should Use This?

General guidance:

• Use 4-bit if you want to run this model alongside other applications
• Use 8-bit if you have the memory and want better quality
• Use 16-bit for research, evaluation, or if memory isn't a concern

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Other Quantization Variants

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Conversion Details

Reproduction

To reproduce this conversion:

pip install mlx-lm==0.31.3
python3 -m mlx_lm.convert --hf-path microsoft/Phi-4-mini-instruct --mlx-path output/Phi-4-mini-instruct-mlx-8bit -q --q-bits 8

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Limitations & Known Issues

• Performance may degrade with very long contexts (>8K tokens) at lower quantization levels.
• This is a weight-only conversion; the model architecture and behavior are inherited from the source model.
• Quantization introduces a small quality loss compared to the original model. Lower bit counts = more loss.
• This model requires Apple Silicon (M1 or later) to run with MLX.

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License

This model conversion inherits the license of the source model: mit.

See the original model card for full license details.

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Changelog

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Converted with MLX Foundry — a professional pipeline for converting models to Apple MLX format.