Instructions to use Shiftedx/qwopus3.6-35b-a3b-coder-abliterated-mxfp4-vision-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shiftedx/qwopus3.6-35b-a3b-coder-abliterated-mxfp4-vision-mlx with MLX:

# Make sure mlx-vlm is installed
# pip install --upgrade mlx-vlm

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
from mlx_vlm.utils import load_config

# Load the model
model, processor = load("Shiftedx/qwopus3.6-35b-a3b-coder-abliterated-mxfp4-vision-mlx")
config = load_config("Shiftedx/qwopus3.6-35b-a3b-coder-abliterated-mxfp4-vision-mlx")

# Prepare input
image = ["http://images.cocodataset.org/val2017/000000039769.jpg"]
prompt = "Describe this image."

# Apply chat template
formatted_prompt = apply_chat_template(
    processor, config, prompt, num_images=1
)

# Generate output
output = generate(model, processor, formatted_prompt, image)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use Shiftedx/qwopus3.6-35b-a3b-coder-abliterated-mxfp4-vision-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 "Shiftedx/qwopus3.6-35b-a3b-coder-abliterated-mxfp4-vision-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": "Shiftedx/qwopus3.6-35b-a3b-coder-abliterated-mxfp4-vision-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Shiftedx/qwopus3.6-35b-a3b-coder-abliterated-mxfp4-vision-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 "Shiftedx/qwopus3.6-35b-a3b-coder-abliterated-mxfp4-vision-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 Shiftedx/qwopus3.6-35b-a3b-coder-abliterated-mxfp4-vision-mlx

Run Hermes

hermes

OpenClaw new

How to use Shiftedx/qwopus3.6-35b-a3b-coder-abliterated-mxfp4-vision-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 "Shiftedx/qwopus3.6-35b-a3b-coder-abliterated-mxfp4-vision-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 "Shiftedx/qwopus3.6-35b-a3b-coder-abliterated-mxfp4-vision-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"

qwopus3.6-35b-a3b-coder-abliterated-mxfp4-vision-mlx

Vision-enabled MLX MXFP4 conversion of Jackrong/Qwopus3.6-35B-A3B-Coder, prepared by Shiftedx for Apple Silicon, MLX, and LM Studio.

Build Notes

Quantized primary language weights with mxfp4 at group size 32.
Kept MoE router and gate modules in affine 8-bit group size 64 for compatibility.
Added Qwen3.5/Qwen3.6-MoE-compatible vision components and validated image grounding locally.
Removed source MTP tensors and set MTP/next-token prediction layer counts to 0 for LM Studio compatibility.
Set tool_parser_type to qwen3_coder.
Patched the chat template so enable_thinking defaults to false when the runtime honors that template variable.
Applied a research refusal-direction weight edit using residual-direction orthogonalization.

Local Validation

Validated locally on June 30, 2026 and July 1, 2026 with LM Studio and direct MLX/VLM loading.

Check	Result
LM Studio load	Passed at 32k context, parallel 1, GPU max.
Basic text completion	Passed; answered `2+2` with `4` and stopped.
Code completion	Passed; produced a simple valid `add(a, b)` function.
Direct MLX/VLM image color smoke	Passed; answered `Red`.
Direct MLX/VLM OCR smoke	Passed; answered `FABLE 42`.
LM Studio OpenAI-compatible image smoke	Passed; answered `Red` and `FABLE 42`.
LM Studio native image smoke	Passed; answered `Red` and `FABLE 42`.
Thinking-off behavior	Smoke checks returned `0` reasoning tokens.
LM Studio logs	No warnings, errors, tracebacks, KV-cache issues, or tokenizer-regex warnings in the validation window.

This is a smoke-validated release, not a full benchmark suite. Broader downstream evaluation is still recommended for production use.

Recommended LM Studio Defaults

After downloading in LM Studio, load the model by repo name:

lms load shiftedx/qwopus3.6-35b-a3b-coder-abliterated-mxfp4-vision-mlx --context-length 32768 --parallel 1 --gpu max

Recommended profile defaults:

Preset/template: Qwen3 thinking-compatible Jinja template with <|im_end|> stop.
Thinking: off by default through the included chat template.
Context length: 200000 when memory allows; 32768 was used for local validation.
Sampling: temperature 0.6, top-k 20, top-p 0.95, min-p enabled at 0.
Repeat penalty: off by default.
Load: parallel 1, GPU max.