Instructions to use Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx 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("Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx")

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 Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx"

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": "Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx 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 "Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx"

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 Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx

Run Hermes

hermes

MLX LM

How to use Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwen3.5-27B — UD-Q6_K_XL (mlx-node)

6-bit base mixed-precision quantization of Qwen/Qwen3.5-27B for Apple Silicon via mlx-node.

	Original (BF16)	This Model
Size	~51 GB	27 GB
Precision	BF16 uniform	Mixed 6/8/8/8/8-bit + BF16

All Variants

Repo	GGUF Equivalent	Size
Brooooooklyn/Qwen3.5-27B-UD-Q2_K_XL-mlx	UD-Q2_K_XL	15 GB
Brooooooklyn/Qwen3.5-27B-UD-Q3_K_XL-mlx	UD-Q3_K_XL	17 GB
Brooooooklyn/Qwen3.5-27B-UD-Q4_K_XL-mlx	UD-Q4_K_XL	20 GB
Brooooooklyn/Qwen3.5-27B-UD-Q5_K_XL-mlx	UD-Q5_K_XL	24 GB
Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx	UD-Q6_K_XL	27 GB
Brooooooklyn/Qwen3.5-27B-UD-Q8_K_XL-mlx	UD-Q8_K_XL	29 GB

Per-Tensor Bit Assignments (N=6)

Weight	Bits
embed_tokens	8-bit
lm_head	8-bit
self_attn.q/k/v_proj	8-bit + AWQ
linear_attn.in_proj_qkv/z	8-bit + AWQ
self_attn.o_proj	bf16
linear_attn.out_proj	bf16
down_proj	8-bit
gate_proj, up_proj	6-bit
Based on Unsloth Dynamic 2.0. Apache 2.0.

Downloads last month: 42

Safetensors

Model size

9B params

Tensor type

BF16

U32

MLX

Hardware compatibility

6-bit

Model tree for Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx

Base model

Qwen/Qwen3.5-27B

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Collection including Brooooooklyn/Qwen3.5-27B-UD-Q6_K_XL-mlx

Qwen-3.5-unsloth-mlx

Collection

AWQ-style pre-scaling using Unsloth's imatrix calibration data, then 3-6-bit affine quantization with the Unsloth mixed-precision recipe via MLX • 20 items • Updated Mar 29 • 20