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"}
     ]
   }'

spicyneuron commited on Mar 2

Commit

01a490c

verified ·

1 Parent(s): 462b773

Update README.md

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README.md +8 -8

README.md CHANGED Viewed

@@ -14,6 +14,14 @@ tags:
 **EDIT:** [v3](https://huggingface.co/spicyneuron/Qwen3-Next-Coder-MLX-mixed-4.5-bit/tree/v3) bumps edge experts to Q8 for further perplexity improvement and minimal effect on speed.
 # Methodology
 Quantized using a custom script inspired by Unsloth/AesSedai/ubergarm style mixed-precision GGUFs.
@@ -26,14 +34,6 @@ This one is comparable to
 [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.
-# 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-mixed-4.5-bit
-```
 # Benchmarks
 - unsloth/Qwen3-Coder-Next-GGUF:UD-Q4_K_XL

 **EDIT:** [v3](https://huggingface.co/spicyneuron/Qwen3-Next-Coder-MLX-mixed-4.5-bit/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-mixed-4.5-bit
+```
 # Methodology
 Quantized using a custom script inspired by Unsloth/AesSedai/ubergarm style mixed-precision GGUFs.
 [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