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

Libraries

How to use nightmedia/Qwen3.5-27B-Text 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("nightmedia/Qwen3.5-27B-Text")

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
LM Studio

Pi new

How to use nightmedia/Qwen3.5-27B-Text with Pi:

Start the MLX server

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

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": "nightmedia/Qwen3.5-27B-Text"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nightmedia/Qwen3.5-27B-Text 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 "nightmedia/Qwen3.5-27B-Text"

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 nightmedia/Qwen3.5-27B-Text

Run Hermes

hermes

MLX LM

How to use nightmedia/Qwen3.5-27B-Text with MLX LM:

Generate or start a chat session

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

Run an OpenAI-compatible server

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

Qwen3.5-27B-Text

This model is Text only: vision was removed, otherwise no changes.

Brainwaves

          arc   arc/e boolq hswag obkqa piqa  wino
qx86-hi   0.443,0.498,0.857,0.701,0.372,0.770,0.752
qx64-hi   0.435,0.494,...
mxfp4     0.460,0.527,0.871,0.694,0.370,0.772,0.752

Quant     Perplexity      Peak Memory
qx86-hi   3.888 ± 0.025   33.34 GB
qx64-hi   3.917 ± 0.025   26.62 GB

Qwen3.5-27B-Text-heretic
qx64-hi   3.931 ± 0.025   26.62 GB

Similar thinking models

TeichAI/Qwen3.5-27b-Opus-4.6-Distill
qx64-hi   0.459,0.542,0.724,0.764,0.402,0.790,0.783

DavidAU/Qwen3.5-27B-Polaris-Advanced-Thinking-Alpha
mxfp4     0.473,0.548,0.709,0.728,0.396,0.777,0.753

DavidAU/Qwen3.5-27B-Claude-4.6-OS-Auto-Variable-Thinking
mxfp8     0.485,0.566,0.875,0.746,0.408,0.789,0.730

Instruct models

DavidAU/Qwen3.5-27B-Claude-4.6-OS-INSTRUCT
mxfp8     0.675,0.827,0.900,0.750,0.496,0.800,0.721
qx86-hi   0.667,0.822,0.900
qx64-hi   0.664,0.820,0.902
mxfp4     0.653,0.815,0.899

Older VL models

Huihui-Qwen3-VL-32B-Thinking-abliterated
qx86-hi   0.376,0.449,0.823,0.637,0.378,0.772,0.681

unsloth-Qwen3-VL-32B-Instruct
qx86x-hi  0.447,0.593,0.904,0.610,0.432,0.738,0.594

Specialized older VL models

nightmedia/Qwen3-32B-Element5-Heretic
qx86-hi   0.483,0.596,0.738,0.754,0.394,0.802,0.710

UIGEN-FX-Agentic-32B
qx86-hi   0.460,0.527,0.681,0.721,0.404,0.790,0.728

More metrics are compiled and will be available soon.

-G

This model Qwen3.5-27B-Text was converted to MLX format from Qwen/Qwen3.5-27B using mlx-lm version 0.30.8.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3.5-27B-Text")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

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