Instructions to use Shiftedx/Qwythos-27B-v1-MXFP8-Vision-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shiftedx/Qwythos-27B-v1-MXFP8-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/Qwythos-27B-v1-MXFP8-Vision-MLX")
config = load_config("Shiftedx/Qwythos-27B-v1-MXFP8-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/Qwythos-27B-v1-MXFP8-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/Qwythos-27B-v1-MXFP8-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/Qwythos-27B-v1-MXFP8-Vision-MLX"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Shiftedx/Qwythos-27B-v1-MXFP8-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/Qwythos-27B-v1-MXFP8-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/Qwythos-27B-v1-MXFP8-Vision-MLX

Run Hermes

hermes

OpenClaw new

How to use Shiftedx/Qwythos-27B-v1-MXFP8-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/Qwythos-27B-v1-MXFP8-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/Qwythos-27B-v1-MXFP8-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"

Qwythos-27B-v1

Qwythos-27B-v1-MXFP8-Vision-MLX

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

Format

Language body: MXFP8, 8-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

This standard artifact intentionally disables the embedded MTP layer and does not include mtp.safetensors.

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.

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.