Instructions to use Shiftedx/ornith-1.5-35b-a3b-attention8-bf16recurrence-vision-mtplx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Shiftedx/ornith-1.5-35b-a3b-attention8-bf16recurrence-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-1.5-35b-a3b-attention8-bf16recurrence-vision-mtplx")
config = load_config("Shiftedx/ornith-1.5-35b-a3b-attention8-bf16recurrence-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-1.5-35b-a3b-attention8-bf16recurrence-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-1.5-35b-a3b-attention8-bf16recurrence-vision-mtplx"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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-1.5-35b-a3b-attention8-bf16recurrence-vision-mtplx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use Shiftedx/ornith-1.5-35b-a3b-attention8-bf16recurrence-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-1.5-35b-a3b-attention8-bf16recurrence-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-1.5-35b-a3b-attention8-bf16recurrence-vision-mtplx

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Shiftedx/ornith-1.5-35b-a3b-attention8-bf16recurrence-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-1.5-35b-a3b-attention8-bf16recurrence-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-1.5-35b-a3b-attention8-bf16recurrence-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 1.5 35B-A3B — MLX Vision + Native MTP

Hybrid MLX quant of ornith-ai/Ornith-1.5-35B-A3B, pinned to revision 10fbf86fed7ecee4a061f8b499a618f46001cac1.

Artifact

4-bit affine expert body with 8-bit attention, routing, embeddings, LM head, and late-layer projections
60 recurrent tensors retained in BF16
333 native BF16 vision tensors
785 native BF16 MTP tensors in mtp/weights.safetensors
Recommended runtime: MTPLX 2.9.1, MLX 0.32.1, MLX-LM 0.31.3
Recommended speculative depth: D3

Run

mtplx serve \
  --model Shiftedx/ornith-1.5-35b-a3b-attention8-bf16recurrence-vision-mtplx \
  --profile sustained \
  --depth 3 \
  --mtp \
  --reasoning off

Local qualification

Tested on an M4 Max with 64 GB unified memory.

Gate	Result
Executable Python tasks	8/8
Text exact-output smoke	pass
Vision smoke	pass
MTP tensor gate	785/785 exact

The final three-prompt, 512-token MTPLX sweep selected D3 at 109.0 decode tok/s. D1, D2, and D3 all passed the quality gate. Fans remained on automatic, so treat the speed as a host-specific measurement rather than a universal benchmark.

Notes

Vision requires a compatible MLX-VLM or MTPLX runtime. Review generated code and tool calls before execution. The parent model and this derivative use the upstream MIT license; see the parent repository for original training and architecture details.

Downloads last month: 115

Safetensors

Model size

35B params

Tensor type

U32

BF16

MLX

Hardware compatibility

4-bit

Model tree for Shiftedx/ornith-1.5-35b-a3b-attention8-bf16recurrence-vision-mtplx

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Quantized

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