Qwen3.5-4B-cagatay-4bit

🐤 Q-Tiny MLX — Qwen 3.5 4B Cagatay (4-bit)

<div align="center">

A 4-bit quantized MLX model for Apple Silicon — fine-tuned for robotics reasoning and instruction following.

8.4 GB → 2.4 GB | 4.5 bits/weight | Runs on MacBook Air

</div>

What is this?

This is the merged + quantized version of cagataydev/qwen3.5-4B-cagatay (LoRA adapter) for native Apple Silicon inference via MLX.

Pipeline: Qwen/Qwen3.5-4B + LoRA adapter → merged weights → MLX 4-bit quantization (group size 64)

🚀 Use with Strands Agents + MLX

The recommended way to use this model is with strands-agents and strands-mlx:

pip install strands-agents strands-agents-mlx

from strands import Agent
from strands_mlx import MLXModel

# Load the 4-bit quantized model
model = MLXModel(model_id="cagataydev/Qwen3.5-4B-cagatay-4bit")

# Create an agent with tools
agent = Agent(model=model)

# Use it!
agent("Plan the steps to pick up a red cube and place it on the shelf")

With Custom Tools

from strands import Agent, tool
from strands_mlx import MLXModel

@tool
def get_robot_state() -> dict:
    """Get the current state of the robot."""
    return {"position": [0.5, 0.3, 0.1], "gripper": "open"}

model = MLXModel(
    model_id="cagataydev/Qwen3.5-4B-cagatay-4bit",
    params={"temperature": 0.7, "max_tokens": 1024}
)

agent = Agent(model=model, tools=[get_robot_state])
agent("What is the robot's current position? Then plan a pick-and-place task.")

With DevDuck

pip install devduck
export MODEL_PROVIDER=mlx
export STRANDS_MODEL_ID=cagataydev/Qwen3.5-4B-cagatay-4bit
devduck

📦 Use with mlx-lm (standalone)

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("cagataydev/Qwen3.5-4B-cagatay-4bit")

messages = [{"role": "user", "content": "Plan how to pick up a cup from the table"}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_dict=False)

response = generate(model, tokenizer, prompt=prompt, verbose=True, max_tokens=512)

CLI

mlx_lm generate --model cagataydev/Qwen3.5-4B-cagatay-4bit --prompt "Hello!"

📊 Model Details

🏋️ Training Provenance

The LoRA adapter was trained with:

🤖 Use Cases

• Robotics task planning — Break down commands into step-by-step action plans
• Embodied reasoning — Spatial understanding and action sequencing
• Edge deployment — 2.4 GB fits comfortably on any Apple Silicon Mac
• Strands agent backbone — Local model for Strands Agents on Mac
• Neon VLA — Part of the Neon VLA vision-language-action stack

📦 Q-Model Family

---

Built with DevDuck 🦆 and Strands Agents 🧬