Instructions to use spicyneuron/Qwen3-Next-Coder-MLX-4.5bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use spicyneuron/Qwen3-Next-Coder-MLX-4.5bit 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("spicyneuron/Qwen3-Next-Coder-MLX-4.5bit")

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 spicyneuron/Qwen3-Next-Coder-MLX-4.5bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "spicyneuron/Qwen3-Next-Coder-MLX-4.5bit"

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": "spicyneuron/Qwen3-Next-Coder-MLX-4.5bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use spicyneuron/Qwen3-Next-Coder-MLX-4.5bit 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 "spicyneuron/Qwen3-Next-Coder-MLX-4.5bit"

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 spicyneuron/Qwen3-Next-Coder-MLX-4.5bit

Run Hermes

hermes

MLX LM

How to use spicyneuron/Qwen3-Next-Coder-MLX-4.5bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "spicyneuron/Qwen3-Next-Coder-MLX-4.5bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "spicyneuron/Qwen3-Next-Coder-MLX-4.5bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "spicyneuron/Qwen3-Next-Coder-MLX-4.5bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwen3-Next-Coder-MLX-4.5bit / README.md

spicyneuron

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	---
	library_name: mlx
	license: apache-2.0
	license_link: https://huggingface.co/Qwen/Qwen3-Coder-Next/blob/main/LICENSE
	pipeline_tag: text-generation
	base_model: Qwen/Qwen3-Coder-Next
	tags:
	- mlx
	---

	[Qwen3-Coder-Next](https://huggingface.co/Qwen/Qwen3-Coder-Next) optimized for MLX. Note: Uses MXFP4 for some module paths.

	EDIT: [v2](https://huggingface.co/spicyneuron/Qwen3-Next-Coder-MLX-4.5bit/tree/v2) fixes some misassigned shared expert gates. Slower, but with 4x better perplexity.

	EDIT: [v3](https://huggingface.co/spicyneuron/Qwen3-Next-Coder-MLX-4.5bit/tree/v3) bumps edge experts to Q8 for further perplexity improvement and minimal effect on speed.

	# Usage

	```sh
	# Start server at http://localhost:8080/v1/chat/completions
	uvx --from mlx-lm mlx_lm.server --host 127.0.0.1 --port 8080 \
	--model spicyneuron/Qwen3-Next-Coder-MLX-4.5bit
	```

	# Methodology

	Quantized using a custom script inspired by Unsloth/AesSedai/ubergarm style mixed-precision GGUFs.
	MLX quantization options differ than llama.cpp, but the principles are the same:
	- Sensitive layers like MoE routing, attention, and output embeddings get higher precision
	- More tolerant layers like MoE experts get lower precision

	This one is comparable to
	~~[Unsloth's UD-Q4_K_XL](https://huggingface.co/unsloth/Qwen3-Coder-Next-GGUF/blob/main/Qwen3-Coder-Next-UD-Q4_K_XL.gguf)~~
	[Unsloth's MOE-MXFP4](https://huggingface.co/unsloth/Qwen3-Coder-Next-GGUF/blob/main/Qwen3-Coder-Next-MXFP4_MOE.gguf)
	in size, but loads and runs noticeably faster thanks to MLX.

	# Benchmarks

	- unsloth/Qwen3-Coder-Next-GGUF:UD-Q4_K_XL
	- mlx-community/Qwen3-Coder-Next-4bit
	- Qwen3-Next-Coder-MLX-mixed-4.5-bit (v1)
	- Qwen3-Next-Coder-MLX-mixed-4.5-bit (v2, ~4.4 bit)
	- Qwen3-Next-Coder-MLX-mixed-4.5-bit (v3, ~4.9 bit)

	## Prompt Processing (tokens/sec)

	\| Prompt Size \| GGUF \| MLX 4bit \| MLX 4.5bit (v1) \| MLX 4.4bit (v2) \| MLX 4.9bit (v3) \|
	\|------------:\|------------:\|----------------:\|-----------------------:\|----------------:\|----------------:\|
	\| 1000 \| 1440.60 \| 1917.29 \| 1894.38 \| 1871.55 \| 1868.77 \|
	\| 5000 \| 1511.29 \| 2113.98 \| 2069.36 \| 2079.87 \| 2071.76 \|
	\| 10000 \| 1491.41 \| 2073.89 \| 2032.13 \| 2039.11 \| 2031.04 \|
	\| 20000 \| 1387.15 \| 1888.56 \| 1854.83 \| 1860.35 \| 1854.24 \|

	## Generation (tokens/sec)

	\| Gen Size \| GGUF \| MLX 4bit \| MLX 4.5b (v1) \| MLX 4.4b (v2) \| MLX 4.9b (v3) \|
	\|---------:\|---------:\|-------------:\|--------------:\|--------------:\|--------------:\|
	\| 500 \| 49.35 \| 76.39 \| 75.30 \| 66.82 \| 67.19 \|
	\| 1000 \| 49.12 \| 74.67 \| 73.16 \| 65.86 \| 64.82 \|
	\| 2000 \| 49.01 \| 71.99 \| 70.95 \| 63.68 \| 62.82 \|
	\| 5000 \| 48.64 \| 67.72 \| 66.67 \| 61.04 \| 60.99 \|

	## Perplexity (MLX Quants)

	\| Model \| Perplexity \| Relative \| Relative % \|
	\|-----------------------\|-----------------\|----------\|------------\|
	\| MLX 4bit \| 4.118 ± 0.021 \| — \| — \|
	\| MLX 4.5bit (v1) \| 4.096 ± 0.021 \| -0.022 \| -0.53% \|
	\| MLX 4.4bit (v2) \| 4.024 ± 0.021 \| -0.094 \| -2.28% \|
	\| MLX 4.9bit (v3) \| 4.016 ± 0.021 \| -0.102 \| -2.48% \|

	```
	# llama.cpp 8130
	llama-bench -fa 1 --batch-size 2048 --ubatch-size 2048 --repetitions 5

	# mlx_lm v0.30.7
	mlx_lm.benchmark --num-trials 5
	mlx_lm.perplexity --sequence-length 1000 --seed 222
	```