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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	---
	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](https://huggingface.co/Qwen/Qwen3.5-4B), optimized for efficient inference on Apple Silicon.

	## Overview

	\| Property \| Value \|
	\|----------\|-------\|
	\| Base Model \| [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) \|
	\| Quantization \| INT4 (group_size=64, affine) \|
	\| Framework \| [MLX](https://github.com/ml-explore/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

	```bash
	pip install mlx mlx-lm
	```

	### Quick Start

	```python
	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

	```bash
	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](https://huggingface.co/Qwen/Qwen3.5-4B).