Instructions to use nightmedia/Qwen3-Next-80B-A3B-Thinking-qx64-hi-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/Qwen3-Next-80B-A3B-Thinking-qx64-hi-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("nightmedia/Qwen3-Next-80B-A3B-Thinking-qx64-hi-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 nightmedia/Qwen3-Next-80B-A3B-Thinking-qx64-hi-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 "nightmedia/Qwen3-Next-80B-A3B-Thinking-qx64-hi-mlx"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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-Next-80B-A3B-Thinking-qx64-hi-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use nightmedia/Qwen3-Next-80B-A3B-Thinking-qx64-hi-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 "nightmedia/Qwen3-Next-80B-A3B-Thinking-qx64-hi-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "nightmedia/Qwen3-Next-80B-A3B-Thinking-qx64-hi-mlx"
# 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-Next-80B-A3B-Thinking-qx64-hi-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use nightmedia/Qwen3-Next-80B-A3B-Thinking-qx64-hi-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 "nightmedia/Qwen3-Next-80B-A3B-Thinking-qx64-hi-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 nightmedia/Qwen3-Next-80B-A3B-Thinking-qx64-hi-mlx

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nightmedia/Qwen3-Next-80B-A3B-Thinking-qx64-hi-mlx with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nightmedia/Qwen3-Next-80B-A3B-Thinking-qx64-hi-mlx"

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 "nightmedia/Qwen3-Next-80B-A3B-Thinking-qx64-hi-mlx" \
  --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"

nightmedia commited on Sep 13, 2025

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@@ -22,7 +22,9 @@ I embarked on a quest to find the smallest quant of the new Qwen3-Next-80B that
 Why? Because someone might hand you a bell, and you'll have no clue why
-Tried everything from q2 up. Notable, the new Qwen model is large(a skinny Harley biker is also a rare find), so there is no need to waste your breath expecting it's going to do the potato-potato on your box.
 This distinction is fine, and models that can make it are exemplary for their recall. In this case it's about quants

 Why? Because someone might hand you a bell, and you'll have no clue why
+Tried everything from q2 up. Notable, the new Qwen model is large(a skinny Harley biker is also a rare find), so there is no need to waste your breath expecting it's going to do the potato-potato on your box.
+It needs at least a 64GB Mac, best if you have more. That's Qwen's design, not mine, I try to make them smaller, while keeping them smart.
 This distinction is fine, and models that can make it are exemplary for their recall. In this case it's about quants