Instructions to use wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit with MLX:

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

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit"

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": "wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Hermes Agent

How to use wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit 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 "wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit"

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 wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit"

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 "wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit" \
  --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-MTPLX

A 4-bit MLX quantization of ornith-ai/Ornith-1.5-35B-A3B, packaged for MTPLX native multi-token speculative decoding on Apple Silicon.

Ornith-1.5-35B-A3B is a 35B-parameter mixture-of-experts model activating approximately 3B parameters per token. This release preserves its native one-layer MTP head and includes the vision tower.

Quantization

Main model: 4-bit affine, group size 64
MTP sidecar: 4-bit affine, group size 64 (mtp.safetensors)
MTP norms: BF16 (non-quantized 1-D tensors)
Vision tower: BF16 (model-vision.safetensors)
Architecture: Qwen3_5MoeForConditionalGeneration
MTP contract: Qwen3-Next / delta norm encoding

The MTP sidecar contains 778 quantized matrix weights and 7 BF16 normalization tensors. It is discovered automatically from the model directory.

Usage with MTPLX

mtplx start cli \
  --model <path-to-this-model>

# Optional: benchmark/tune the MTP draft depth
mtplx tune --model <path-to-this-model> --retune

The model can also be loaded by MLX-LM for autoregressive text generation, without MTPLX speculative decoding.

Provenance and licensing

Base model: ornith-ai/Ornith-1.5-35B-A3B — MIT
Runtime: MTPLX — Apache-2.0
Conversion: MLX affine quantization, group size 64

This repository contains a quantized derivative of the base model. Please follow the upstream model license and usage conditions. MTPLX attribution is required when shipping a product or service built on MTPLX:

Powered by MTPLX
https://github.com/youssofal/MTPLX

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Safetensors

Model size

5B params

Tensor type

BF16

U32

MLX

Hardware compatibility

4-bit

Model tree for wang-yang/Ornith-1.5-35B-A3B-MTPLX-4bit

Base model

ornith-ai/Ornith-1.5-35B-A3B

Quantized

(87)

this model