Instructions to use Shiftedx/Qwythos-27B-v1-MXFP4-Vision-MTPLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shiftedx/Qwythos-27B-v1-MXFP4-Vision-MTPLX 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/Qwythos-27B-v1-MXFP4-Vision-MTPLX")
config = load_config("Shiftedx/Qwythos-27B-v1-MXFP4-Vision-MTPLX")

# 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/Qwythos-27B-v1-MXFP4-Vision-MTPLX 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/Qwythos-27B-v1-MXFP4-Vision-MTPLX"

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/Qwythos-27B-v1-MXFP4-Vision-MTPLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Shiftedx/Qwythos-27B-v1-MXFP4-Vision-MTPLX 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/Qwythos-27B-v1-MXFP4-Vision-MTPLX"

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/Qwythos-27B-v1-MXFP4-Vision-MTPLX

Run Hermes

hermes

OpenClaw new

How to use Shiftedx/Qwythos-27B-v1-MXFP4-Vision-MTPLX 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/Qwythos-27B-v1-MXFP4-Vision-MTPLX"

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/Qwythos-27B-v1-MXFP4-Vision-MTPLX" \
  --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"

Qwythos-27B-v1

Qwythos-27B-v1-MXFP4-Vision-MTPLX

MLX/VLM conversion of empero-ai/Qwythos-27B-v1, pinned to revision 7c72a9c714cf66281cb222c4aa0aef368d84c94f.

Format

Language body: MXFP4, 4-bit, group size 32
Vision tower: source BF16, 333 tensors
Architecture: dense Qwen3.5 multimodal, 64 language layers
Chat template and tokenizer: preserved from the pinned source
Packaging: self-contained; no companion model repository is required

Local qualification

Structural inspection: pass (1,682 indexed tensors; 498 quantized weights; no stale or missing shards)
Strict MLX-VLM load: pass
Text behavior: 5/5 exact matches against BF16 for identity, JSON, code, tool calling, and loop tasks
Vision smoke: pass; post-graft vision features are bit-for-bit equal to BF16
Long-context needle: exact at 19,069 input tokens

The source configuration's 1M-token YaRN settings are preserved, but this release was locally exercised only through 19,069 input tokens—not at 1M.

MTPLX runtime

This variant packages the complete MXFP4 body and vision tower with the source model's native 15-tensor BF16 MTP head for MTPLX 2.0.1. Depth 2 is the recommended setting.

mtplx inspect --require-mtp <local-path-or-repo-id>
mtplx serve --model <local-path-or-repo-id> --generation-mode mtp --mtp-depth 2 --reasoning-mode off

Local 192-token tuning measured 44.96 decode tok/s at depth 2 versus 25.39 autoregressive (1.77x). In a deterministic 10-prompt API comparison, depth 2 and autoregressive outputs were byte-identical on all 10 prompts; both passed the same 6/10 validators. Mean decode throughput was 49.45 versus 27.87 tok/s. A separate code smoke passed 3/3. Thermal fan control remained on auto.

Basic use

python -m mlx_vlm.generate \
  --model <local-path-or-repo-id> \
  --image image.jpg \
  --prompt "Describe this image." \
  --max-tokens 256

Review the source model card for intended use, training, license, and deployment considerations. This conversion does not change the source model's safety characteristics.