Instructions to use Shiftedx/ornith-1.0-35b-mxfp4-vision-mtplx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shiftedx/ornith-1.0-35b-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/ornith-1.0-35b-mxfp4-vision-mtplx")
config = load_config("Shiftedx/ornith-1.0-35b-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/ornith-1.0-35b-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/ornith-1.0-35b-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/ornith-1.0-35b-mxfp4-vision-mtplx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use Shiftedx/ornith-1.0-35b-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/ornith-1.0-35b-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/ornith-1.0-35b-mxfp4-vision-mtplx

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Shiftedx/ornith-1.0-35b-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/ornith-1.0-35b-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/ornith-1.0-35b-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"

Ornith 1.0

ornith-1.0-35b-mxfp4-vision-mtplx

Vision-preserving MLX conversion of deepreinforce-ai/Ornith-1.0-35B, pinned to revision 5df2ed3f675c7beaa490328cc70bb573b65fb660.

Format

Language body: MXFP4, 4-bit, group size 32
MoE gate overrides: affine 8-bit, group size 64
Vision tower: source BF16, 333 tensors
Architecture: Qwen3.5 MoE multimodal, 40 language layers, 256 experts
Tokenizer, chat template, processor metadata, and vision features preserved from the pinned source

Local qualification

Structural inspection: pass (1,658 indexed tensors; 512 quantized weights; 80 MoE gate overrides)
Shard index: pass; no stale or missing shards
Strict MLX-LM and MLX-VLM lazy load: pass
Cross-variant tokenizer, template, processor, and vision-shard parity: pass
Vision features: source BF16 tensor payload preserved

MTPLX runtime

This self-contained experimental variant combines the qualified MXFP4 Ornith body and BF16 vision tower with a compatible Qwen3.5-MoE Q5/G64 MTP sidecar. Ornith's upstream 35B checkpoint does not include native MTP tensors. The sidecar is byte-identical to mtp.safetensors from Shiftedx/ornith-1.0-35b-abliterated-mxfp4-vision-mtplx at revision afed7f14a357c9db38bf67eb5b7c66b6152157f5 (SHA-256 d9788ed667167b211cdb178247f4d5ccc2213f349611d7c05486d23f6b364b84).

MTPLX 2.0.2 inspection and tensor-contract gates passed. In the frozen one-prompt performance test, depth 2 achieved 136.94 decode tok/s versus 89.41 tok/s autoregressive (+53.17%); end-to-end throughput improved from 82.89 to 123.84 tok/s (+49.40%). Depth-2 acceptance was 94.51% and 87.78% by draft depth. Quality gates passed, but every completion reached the 256-token ceiling, fans remained on automatic, and the figures are a point estimate rather than a statistical benchmark.

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

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 upstream model card for intended use, training, license, benchmarks, and deployment considerations. Quantization does not change the upstream model's safety characteristics.