Image-Text-to-Text
MLX
Safetensors
qwen3_5_moe
anura
genysis-iq
agentic
coding
vision
tool-use
conversational
4-bit precision
Instructions to use wsgpolar/Anura-R1-Ultra with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
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
- Pi
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"
Download training/adapters/dev-test/train_args.json from wsgpolar/Anura-R1-Ultra: direct link, hf CLI and curl.
- Browser
- Download file 504 Bytes
-
https://huggingface.co/wsgpolar/Anura-R1-Ultra/resolve/main/training/adapters/dev-test/train_args.json
- Command line
-
hf download hf://wsgpolar/Anura-R1-Ultra/training/adapters/dev-test/train_args.json
-
curl -L -o train_args.json https://huggingface.co/wsgpolar/Anura-R1-Ultra/resolve/main/training/adapters/dev-test/train_args.json
504 Bytes
| { | |
| "model": "models/dev/Qwen3.5-0.8B-4bit", | |
| "data": "data/dev_train.jsonl", | |
| "valid": "data/dev_valid.jsonl", | |
| "out": "models/adapters/dev-test", | |
| "rank": 16, | |
| "lora_layers": 8, | |
| "alpha": 32, | |
| "dropout": 0.05, | |
| "lr": 0.0002, | |
| "min_lr": 5e-06, | |
| "epochs": 2.0, | |
| "warmup": 5, | |
| "batch_tokens": 4096, | |
| "max_len": 4096, | |
| "grad_accum": 1, | |
| "grad_clip": 1.0, | |
| "grad_checkpoint": true, | |
| "gdn_chunk": 32, | |
| "eval_every": 20, | |
| "save_every": 20, | |
| "seed": 0, | |
| "resume": null, | |
| "max_steps": 40 | |
| } |