Instructions to use barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit 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("barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit")

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 barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit"

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": "barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit 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 "barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit"

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 barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit"

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 "barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit" \
  --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"

Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MLX-3bit

MLX 3-bit conversion of barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MTP -- the MTP-grafted variant of the REAP-pruned (205/256 experts) Qwen/Qwen3.6-35B-A3B MoE fine-tuned on Claude Opus reasoning traces -- for Apple Silicon. Sibling formats:

bf16 safetensors source: barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MTP
plain merge (no MTP head): barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill
GGUF quantizations: barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-GGUF
other MLX quant: barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MTP-MLX-4bit

Converted straight from the bf16 safetensors weights (not from any GGUF quant).

Conversion details

Bits: 3 (group_size=64, affine) — experimental quality tier
Command: python -m mlx_lm.convert --hf-path barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MTP --mlx-path <out> --quantize --q-bits 3 --q-group-size 64
Tooling: mlx 0.32.1 · mlx-lm 0.31.3 · hub 1.27.0; source commit 9fa24b516dd3
Converter: mlx-lm
Environment: Google Colab G4 (RTX PRO 6000 Blackwell 96 GB), MLX cuda12 backend
Full conversion log ships in this repo as conversion_log.txt

MTP head note

The source checkpoint carries an MTP head (mtp_num_hidden_layers: 1). Standard MLX loaders do not use it: this conversion dönüştürücü tarafından düşürüldü (beklenen davranış; MTPLX build'i için bkz. mtplx forge). These builds therefore behave identically to the plain-merge checkpoint at inference time. For native-MTP speculative decoding on Apple Silicon, build an MTPLX artifact from the source repo with mtplx forge (github.com/youssofal/MTPLX).

Usage

pip install -U mlx-lm

# text-only chat
python -m mlx_lm.generate \
  --model barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MTP-MLX-3bit \
  --prompt "Explain the Monty Hall problem briefly." \
  --max-tokens 512

# OpenAI-compatible local server
python -m mlx_lm.server \
  --model barozp/Qwen3.6-29B-REAP-Opus-Reasoning-Distill-MTP-MLX-3bit

About the source model

A REAP-pruned (205/256 experts) Qwen3.6-35B-A3B MoE (~28.24B total, ~3B active per token), LoRA fine-tuned on Claude Opus reasoning traces, then merged. Key result from the source card: ARC-Challenge above the unpruned base (0.616 vs 0.532) with 96.9% MMLU retention at 20% expert pruning. Method paper: arXiv:2510.13999.