Instructions to use nightmedia/Qwen3-Coder-Next-mxfp4-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/Qwen3-Coder-Next-mxfp4-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-Coder-Next-mxfp4-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-Coder-Next-mxfp4-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-Coder-Next-mxfp4-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-Coder-Next-mxfp4-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use nightmedia/Qwen3-Coder-Next-mxfp4-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-Coder-Next-mxfp4-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "nightmedia/Qwen3-Coder-Next-mxfp4-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-Coder-Next-mxfp4-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use nightmedia/Qwen3-Coder-Next-mxfp4-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-Coder-Next-mxfp4-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-Coder-Next-mxfp4-mlx

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nightmedia/Qwen3-Coder-Next-mxfp4-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-Coder-Next-mxfp4-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-Coder-Next-mxfp4-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"

mxfp4 produces garbage (solved)

by diggingAI - opened Feb 4

Discussion

diggingAI

Feb 4

After 30-40 lines of output the model produces garbage. Settings: temperature: 1.0, top_p: 0.95, top_k: 40. Latest ml-explore/mlx-lm,

python3 -m mlx_lm.server --model nightmedia/Qwen3-Coder-Next-mxfp4-mlx --temp 1.0 --top-p 0.95 --top-k 40

Other models work flawlessly.

nightmedia

Owner Feb 4

Interesting, did you try it in LMStudio? also, update the local MLX tools, they keep changing every day now. I was not able to replicate issues with my setup, and I use the latest codebase from main

diggingAI

Feb 4

I did a fresh git clone with a venv and all latest packages. I did not try with LMStudio as it wanted to re-download again as it expects the models in ~/.lmstudio. Would a symlink do it? I may try again one of the next days, currently I am downloading the mlx-community quant.

nightmedia

Owner Feb 4

•

edited Feb 4

yeah, symlink should work, I do that often.

I went through the pyenv nightmare a few times. Hard to stabilize an environment on mlx if you try to do more than one thing at a time. I ended up with a merge environment, a quant environment, etc... it gets crazy after a while

LMStudio is always stable with running quants because it embeds a safe mlx version

diggingAI

Feb 4

•

edited Feb 4

It does indeed work with LMStudio and it also works with pip install mlx==0.30.1 mlx-metal==0.30.1. And also the mlx-community quant produces similar garbage, so definitely not your quant the reason. Will see if I find some time the next days to pinpoint the problem or at least write a decent but report for mlx-lm.
For info on M1 64GB with mxfp4 I get ~300Tok/s PP and ~40Tok/s TG with 5000 context.

nightmedia

Owner Feb 5

Excellent, thank you for confirming it. Should also check out the qx64-hi I just uploaded, about same performance level

diggingAI

Feb 5

I created an issue: https://github.com/ml-explore/mlx-lm/issues/844

diggingAI

Feb 5

Looks like an M1/M2 issue and a fix is in the pipeline for merge with main: https://github.com/ml-explore/mlx/pull/3099
Once it is merged I'll give it a test run.

diggingAI changed discussion title from mxfp4 produces garbage to mxfp4 produces garbage (solved) Mar 25

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