Instructions to use majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4 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("majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4")

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 majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4"

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": "majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4 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 "majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4"

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 majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4

Run Hermes

hermes

OpenClaw new

How to use majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4"

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 "majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4" \
  --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 majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4 with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4

Summary

RotorQuant + MLX-NVFP4 (4-bit) variant of Qwen/Qwen3.6-35B-A3B.

Why this variant

Apple Silicon (M1/M2/M3/M4) with RotorQuant structural pre-conditioning and MLX-native NVFP4 layout (E2M1 weights, per-16-element FP8 (NVIDIA Blackwell layout)). 4.503 bits/weight, ~18 GB on disk, sub-2-s load on M4 Max. Pick this over the affine MLX variants when you want NVFP4 format parity with hardware pipelines while running locally.

Quickstart

import mlx.core as mx
from mlx_lm import load, generate

model, tokenizer = load("majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-NVFP4")

prompt = "Give me a short introduction to Mixture-of-Experts models."
response = generate(model, tokenizer, prompt=prompt, max_tokens=512)
print(response)

Text-only extraction. This repo contains only the quantized language tower of Qwen3.6-35B-A3B. The upstream vision tower (333 tensors) and MTP head are not included, so image/video input does not work and mlx_vlm.load(...) fails with a Missing ... parameters error (the vision tower it expects is absent from the checkpoint). Load it with mlx_lm (recent version with qwen3_5_moe support) as shown above. For image/video input, use the upstream BF16 model Qwen/Qwen3.6-35B-A3B on a runtime that supports it.

Hardware compatibility

Device	VRAM	Recommendation
Apple M4 Max 128 GB	~21 GB	recommended — headroom for long context
Apple M3 Max 64 GB	~21 GB	fits comfortably
Apple M2 Max 32 GB	~21 GB	tight — short context only

Reproduce

# dequantize from the rotor/turbo MLX-8bit source, then re-quantize
python -c "from mlx_lm import convert; convert(hf_path=\"majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-8bit\", mlx_path=\"bf16\", dequantize=True, trust_remote_code=True)"
python -c "from mlx_lm import convert; convert(hf_path=\"bf16\", mlx_path=\"out-nvfp4\", quantize=True, q_bits=4, q_group_size=16, q_mode=\"nvfp4\", trust_remote_code=True)"

Reproduced at commit 919836a.

Evaluation

benchmarks pending — populated after the eval-harness workstream lands.

Family

bf16 — Qwen/Qwen3.6-35B-A3B
FP8 card — majentik/Qwen3.6-35B-A3B-FP8
RotorQuant MLX-4bit (affine) — majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-4bit
RotorQuant MLX-8bit (source for this) — majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-8bit
plain MLX-NVFP4 (no rotor/turbo) — majentik/Qwen3.6-35B-A3B-MLX-NVFP4

Provenance

Source SHA: majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-8bit
Calibration hash: none (nvfp4 is calibration-free; rotor/turbo conditioning inherited from source)
Uploaded: 2026-04-21T06:17:30.021158+00:00

Toolchain:

huggingface_hub: 1.11.0
mlx: 0.31.1
mlx-lm: 0.31.2

License

Released under apache-2.0. Upstream license of the base model applies.

Downloads last month: 468

Safetensors

Model size

35B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

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