Instructions to use abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-mxfp4-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-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("abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-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 abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-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 "abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-mxfp4-mlx"

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": "abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-mxfp4-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-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 "abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-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 "abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-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"

MLX LM

How to use abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-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 "abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-mxfp4-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-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": "abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-mxfp4-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-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 "abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-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 abnormalmapstudio/Qwen3-Next-80B-A3B-Thinking-mxfp4-mlx

Run Hermes

hermes

Qwen3-Next-80B-A3B-Thinking-mxfp4-mlx / README.md

abnormalmapstudio

Add MLX 4-bit (mxfp4) quantized checkpoint and configs

88c66ae 11 months ago

2.79 kB

license: apache-2.0
library_name: mlx
pipeline_tag: text-generation
base_model: Qwen/Qwen3-Next-80B-A3B-Thinking
tags:
  - mlx
  - qwen3_next
  - 4-bit
  - mxfp4
  - text-generation
quantization_config:
  bits: 4
  mode: mxfp4
  group_size: 32
model-index:
  - name: Qwen3-Next-80B-A3B-Thinking mxfp4 (MLX 4-bit)
    results: []

Qwen3-Next-80B-A3B-Thinking — MLX 4-bit (mxfp4)

This repository provides an Apple MLX-optimized 4-bit mxfp4 quantized checkpoint of the base model Qwen/Qwen3-Next-80B-A3B-Thinking for fast, memory‑efficient local inference on Apple Silicon.

Key details

Format: MLX runtime, safetensors sharded weights
Quantization: mxfp4, group_size=32 with selective 8‑bit gates
Task: text generation / chat
Tokenizer: provided via tokenizer.json (BPE) with chat_template.jinja

Files

model-0000X-of-00009.safetensors and model.safetensors.index.json — weights (LFS)
config.json — architecture and quantization keys for MLX loaders
tokenizer.json, tokenizer_config.json, vocab.json, merges.txt, added_tokens.json, special_tokens_map.json
chat_template.jinja — chat formatting for multi‑turn prompts
generation_config.json — sensible default generation params
LICENSE — Apache-2.0

Usage (MLX)

Install MLX-LM and run generation:

pip install mlx-lm

from mlx_lm import load, generate

# Replace with your repo id once pushed
repo_id = "<your-username>/Qwen3-Next-80B-A3B-Thinking-mxfp4-mlx"

model, tokenizer = load(repo_id)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Explain what mxfp4 quantization is in one paragraph."},
]

prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)

out = generate(
    model,
    tokenizer,
    prompt,
    max_tokens=512,
    temp=0.7,
    top_p=0.95,
)
print(out)

CLI example:

mlx_lm.generate --model "<your-username>/Qwen3-Next-80B-A3B-Thinking-mxfp4-mlx" \
  --prompt "List 5 creative dinner ideas." --max-tokens 200

Hardware Notes

Apple Silicon recommended (M2/M3). The 4‑bit checkpoint reduces memory pressure significantly, but 80B‑class models still require substantial VRAM and system RAM. For best results, ensure macOS swap is available and limit max_tokens accordingly.

License

License: Apache-2.0 (this quantized packaging). See LICENSE.
Base model: Qwen/Qwen3-Next-80B-A3B-Thinking (see its model card for upstream license and usage terms). Ensure your use complies with the base model’s license.

Attribution

If you use this model, please credit the upstream Qwen team and note that this is an MLX 4‑bit mxfp4 quantized derivative for Apple Silicon.