Instructions to use OsaurusAI/Ornith-1.0-9B-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OsaurusAI/Ornith-1.0-9B-MXFP4 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("OsaurusAI/Ornith-1.0-9B-MXFP4")
config = load_config("OsaurusAI/Ornith-1.0-9B-MXFP4")

# 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 OsaurusAI/Ornith-1.0-9B-MXFP4 with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/Ornith-1.0-9B-MXFP4"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "OsaurusAI/Ornith-1.0-9B-MXFP4"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use OsaurusAI/Ornith-1.0-9B-MXFP4 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 "OsaurusAI/Ornith-1.0-9B-MXFP4"

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 OsaurusAI/Ornith-1.0-9B-MXFP4

Run Hermes

hermes

OpenClaw new

How to use OsaurusAI/Ornith-1.0-9B-MXFP4 with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "OsaurusAI/Ornith-1.0-9B-MXFP4"

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 "OsaurusAI/Ornith-1.0-9B-MXFP4" \
  --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-1.0-9B-MXFP4

File size: 1,648 Bytes

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---
license: mit
base_model: deepreinforce-ai/Ornith-1.0-9B
pipeline_tag: image-text-to-text
library_name: mlx
tags: [ornith, qwen3_5, vl, vision-language, gated-deltanet, linear-attention, mlx, mxfp4, osaurus]
---

![Osaurus](osaurus-x-banner.png)

# Ornith-1.0-9B · MXFP4

Official **OsaurusAI** MXFP4 build of [deepreinforce-ai/Ornith-1.0-9B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B) (MIT) — a **vision-language** model on a Qwen3.5 hybrid backbone. Near-lossless 8-bit microscaled FP; runs on Apple Silicon via [Osaurus](https://osaurus.ai) / mlx.

- **~5.3 GB (from ~18.8 GB bf16)** bundle.
- **MXFP8**: microscaled FP4 (group-size 32, 4-bit) on the language-model linear weights; the **vision tower is preserved at fp16**, short-conv kernels and norms kept fp16.
- **Vision-language** (image + text → text).

## Architecture

| | |
|---|---|
| Family | `qwen3_5` (dense, hybrid) |
| Text layers | 32 — **24 Gated-DeltaNet (linear-attention)** + **8 full-attention** |
| Hidden | 4096 · untied lm_head |
| Vision | ViT tower (`model.visual`) preserved fp16 |
| Cache | hybrid (GDN state + KV for attention layers) |

## Usage
```bash
# text
python -m mlx_lm generate --model OsaurusAI/Ornith-1.0-9B-MXFP4 --prompt "Explain a hash map in two sentences."
```
For image+text, load in [Osaurus](https://osaurus.ai) or an MLX-VLM runtime that supports `qwen3_5` vision.

## Provenance
- Base: [deepreinforce-ai/Ornith-1.0-9B](https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B) © DeepReinforce — MIT (Qwen3.5-based)
- Quantization: Osaurus · MXFP4 (microscaled FP4, group-size 32; vision tower fp16) · eric@osaurus.ai