Instructions to use pyromind/qwen3.5-4b-debug-mlx-int4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use pyromind/qwen3.5-4b-debug-mlx-int4 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("pyromind/qwen3.5-4b-debug-mlx-int4")

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 pyromind/qwen3.5-4b-debug-mlx-int4 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "pyromind/qwen3.5-4b-debug-mlx-int4"

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": "pyromind/qwen3.5-4b-debug-mlx-int4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use pyromind/qwen3.5-4b-debug-mlx-int4 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "pyromind/qwen3.5-4b-debug-mlx-int4"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "pyromind/qwen3.5-4b-debug-mlx-int4"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "pyromind/qwen3.5-4b-debug-mlx-int4",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use pyromind/qwen3.5-4b-debug-mlx-int4 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 "pyromind/qwen3.5-4b-debug-mlx-int4"

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 pyromind/qwen3.5-4b-debug-mlx-int4

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use pyromind/qwen3.5-4b-debug-mlx-int4 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "pyromind/qwen3.5-4b-debug-mlx-int4"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "pyromind/qwen3.5-4b-debug-mlx-int4" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

qwen3.5-4b-debug-mlx-int4 / README.md

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metadata

language: en
pipeline_tag: text-generation
library_name: mlx
tags:
  - mlx
  - qwen
  - int4
  - quantized
base_model: Qwen/Qwen3.5-4B

Qwen3.5-4B MLX INT4

This is an MLX INT4 quantized version of Qwen/Qwen3.5-4B, optimized for efficient inference on Apple Silicon.

Overview

Property	Value
Base Model	Qwen/Qwen3.5-4B
Quantization	INT4 (group_size=64, affine)
Framework	MLX
Target Hardware	Apple Silicon (M1/M2/M3/M4)
Original Size	~9 GB
Quantized Size	~4.2 GB

Quantization Details

Method: Post-training INT4 quantization via mlx_lm
Group Size: 64
Mode: Affine
Protected Layers: embed_tokens and lm_head kept at float16 for output quality
Compression: 53% reduction

Usage

Requirements

pip install mlx mlx-lm

Quick Start

from mlx_lm import load, generate

model, tokenizer = load("pyromind/qwen3.5-4b-debug-mlx-int4")

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain quantum computing in simple terms."},
]

prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

output = generate(
    model, tokenizer,
    prompt=prompt,
    max_tokens=2048,
    temp=0.6,
)
print(output)

Chat via CLI

mlx_lm.chat --model pyromind/qwen3.5-4b-debug-mlx-int4

License

This model follows the license of the base model Qwen/Qwen3.5-4B.