Instructions to use wsgpolar/Anura-R1-Ultra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wsgpolar/Anura-R1-Ultra 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("wsgpolar/Anura-R1-Ultra")
config = load_config("wsgpolar/Anura-R1-Ultra")

# 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 wsgpolar/Anura-R1-Ultra with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "wsgpolar/Anura-R1-Ultra"

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": "wsgpolar/Anura-R1-Ultra"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent

How to use wsgpolar/Anura-R1-Ultra 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 "wsgpolar/Anura-R1-Ultra"

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 wsgpolar/Anura-R1-Ultra

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use wsgpolar/Anura-R1-Ultra with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "wsgpolar/Anura-R1-Ultra"

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 "wsgpolar/Anura-R1-Ultra" \
  --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"

Anura-R1-Ultra / NOTICE

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Anura R1 Ultra
Copyright (c) 2026 Genysis IQ

Anura R1 Ultra is built by fine-tuning an open-weight foundation model. This NOTICE
records the third-party works it is derived from or depends on, as their licenses require.

Foundation model
----------------
Qwen3.6-35B-A3B — Copyright (c) Alibaba Cloud (Qwen Team).
Licensed under the Apache License, Version 2.0.
https://huggingface.co/Qwen/Qwen3.6-35B-A3B
MLX 4-bit conversion published by the mlx-community organisation on Hugging Face
(https://huggingface.co/mlx-community/Qwen3.6-35B-A3B-4bit), same license.

Anura's LoRA adapter and its packaged model are modifications of that work: the
foundation weights are unchanged and a low-rank adapter trained by Genysis IQ is applied
at load time.

A copy of the Apache License 2.0 is available at http://www.apache.org/licenses/LICENSE-2.0

Software dependencies (each under its own license)
--------------------------------------------------
MLX, mlx-lm (MIT) — Apple Inc.
mlx-vlm (MIT) — Prince Canuma and contributors
transformers, tokenizers (Apache-2.0) — Hugging Face
FastAPI (MIT), Uvicorn (BSD-3-Clause), httpx (BSD-3-Clause)
trafilatura (Apache-2.0), selectolax (MIT), ddgs (MIT), pypdf (BSD-3-Clause)
Rich (MIT), prompt_toolkit (BSD-3-Clause), Pillow (MIT-CMU), PyYAML (MIT)

	Anura R1 Ultra
	Copyright (c) 2026 Genysis IQ

	Anura R1 Ultra is built by fine-tuning an open-weight foundation model. This NOTICE
	records the third-party works it is derived from or depends on, as their licenses require.

	Foundation model
	----------------
	Qwen3.6-35B-A3B — Copyright (c) Alibaba Cloud (Qwen Team).
	Licensed under the Apache License, Version 2.0.
	https://huggingface.co/Qwen/Qwen3.6-35B-A3B
	MLX 4-bit conversion published by the mlx-community organisation on Hugging Face
	(https://huggingface.co/mlx-community/Qwen3.6-35B-A3B-4bit), same license.

	Anura's LoRA adapter and its packaged model are modifications of that work: the
	foundation weights are unchanged and a low-rank adapter trained by Genysis IQ is applied
	at load time.

	A copy of the Apache License 2.0 is available at http://www.apache.org/licenses/LICENSE-2.0

	Software dependencies (each under its own license)
	--------------------------------------------------
	MLX, mlx-lm (MIT) — Apple Inc.
	mlx-vlm (MIT) — Prince Canuma and contributors
	transformers, tokenizers (Apache-2.0) — Hugging Face
	FastAPI (MIT), Uvicorn (BSD-3-Clause), httpx (BSD-3-Clause)
	trafilatura (Apache-2.0), selectolax (MIT), ddgs (MIT), pypdf (BSD-3-Clause)
	Rich (MIT), prompt_toolkit (BSD-3-Clause), Pillow (MIT-CMU), PyYAML (MIT)