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 / README.md

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---
license: other
license_name: genysis-iq-commercial
license_link: LICENSE
library_name: mlx
pipeline_tag: image-text-to-text
tags:
- anura
- genysis-iq
- agentic
- coding
- mlx
- vision
- tool-use
---
# Anura R1 Ultra

**Anura R1 Ultra** is an agentic coding model by **Genysis IQ** (version 1.0.0).

- Agentic software engineering: reads and edits code, runs commands and tests, plans multi-step work
- Web search and crawling for current information, with cited sources
- Vision: screenshots, diagrams, UI mockups, charts and photos
- Fast local inference on Apple Silicon with MLX (mixture-of-experts, ~3B active parameters per token)
- 128K-token context in the default engine configuration

## Results (measured on an Apple M5, 32 GB)

| | Anura R1 Ultra |
|---|---|
| Identity, held-out questions (Anura system prompt / served with identity preamble) | 26/26 · 26/26 |
| Identity, bare weights under a generic third-party prompt | 24/26 |
| Vision (shapes, OCR, code screenshot, chart, UI→HTML) | 5/5 |
| Agent coding tasks, pass@1, 40 verified tasks in 6 languages | 36/40 |
| Decode speed | ~48 tok/s |
| Cached follow-up prefill (14.7K-token prompt) | 0.18 s |

The coding tasks are the suite the training trajectories came from, so they show the fine-tune kept the
foundation's skills; they are not a held-out benchmark.

## Format

MLX 4-bit weights plus the Anura R1 Ultra LoRA adapter in `anura_adapter/`, applied at load time. The chat
template is Anura R1 Ultra's own (`chat_template.jinja`). About 20 GB of memory is needed (32 GB Apple
Silicon Mac recommended).

## Running it

The intended runtime is the Anura engine and CLI from Genysis IQ (`anura serve`, `anura`), which adds the
agent tools, sessions, web UI and an OpenAI-compatible API. To load the weights directly with mlx-vlm:

```python
from mlx_vlm import load, generate
model, processor = load("<this repo>", adapter_path="<this repo>/anura_adapter")
```

## License and provenance

Access to this repository is limited to licensed users of Genysis IQ. Genysis IQ built Anura R1 Ultra by
fine-tuning an open-weight foundation model released under the Apache License 2.0; those foundation weights
remain under that license, and `NOTICE` gives the attribution it requires. Genysis IQ's adapter and packaging
are provided under Genysis IQ's commercial terms.

	---
	license: other
	license_name: genysis-iq-commercial
	license_link: LICENSE
	library_name: mlx
	pipeline_tag: image-text-to-text
	tags:
	- anura
	- genysis-iq
	- agentic
	- coding
	- mlx
	- vision
	- tool-use
	---

	# Anura R1 Ultra

	Anura R1 Ultra is an agentic coding model by Genysis IQ (version 1.0.0).

	- Agentic software engineering: reads and edits code, runs commands and tests, plans multi-step work
	- Web search and crawling for current information, with cited sources
	- Vision: screenshots, diagrams, UI mockups, charts and photos
	- Fast local inference on Apple Silicon with MLX (mixture-of-experts, ~3B active parameters per token)
	- 128K-token context in the default engine configuration

	## Results (measured on an Apple M5, 32 GB)

	\| \| Anura R1 Ultra \|
	\|---\|---\|
	\| Identity, held-out questions (Anura system prompt / served with identity preamble) \| 26/26 · 26/26 \|
	\| Identity, bare weights under a generic third-party prompt \| 24/26 \|
	\| Vision (shapes, OCR, code screenshot, chart, UI→HTML) \| 5/5 \|
	\| Agent coding tasks, pass@1, 40 verified tasks in 6 languages \| 36/40 \|
	\| Decode speed \| ~48 tok/s \|
	\| Cached follow-up prefill (14.7K-token prompt) \| 0.18 s \|

	The coding tasks are the suite the training trajectories came from, so they show the fine-tune kept the
	foundation's skills; they are not a held-out benchmark.

	## Format

	MLX 4-bit weights plus the Anura R1 Ultra LoRA adapter in `anura_adapter/`, applied at load time. The chat
	template is Anura R1 Ultra's own (`chat_template.jinja`). About 20 GB of memory is needed (32 GB Apple
	Silicon Mac recommended).

	## Running it

	The intended runtime is the Anura engine and CLI from Genysis IQ (`anura serve`, `anura`), which adds the
	agent tools, sessions, web UI and an OpenAI-compatible API. To load the weights directly with mlx-vlm:

	```python
	from mlx_vlm import load, generate

	model, processor = load("<this repo>", adapter_path="<this repo>/anura_adapter")
	```

	## License and provenance

	Access to this repository is limited to licensed users of Genysis IQ. Genysis IQ built Anura R1 Ultra by
	fine-tuning an open-weight foundation model released under the Apache License 2.0; those foundation weights
	remain under that license, and `NOTICE` gives the attribution it requires. Genysis IQ's adapter and packaging
	are provided under Genysis IQ's commercial terms.