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
- MLX
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
- Pi
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 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_tokensandlm_headkept 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.
- Downloads last month
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Model size
2B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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4-bit