AtomicChat/

Ornith-9B-MLX-4bit

Base model: deepreinforce-ai/Ornith-1.0-9B

Highlights

• 0.0B parameters: the weights this repo quantizes.
• Context length: 262,144 tokens (256K), as published by DeepReinforce.
• 32 layers: Dense decoder.
• Modalities: Text, Image.
• Full imatrix ladder: every quant is calibrated with an importance matrix.
• State-of-the-Art Coding Agents: Available in 9B-Dense, 31B-Dense, 35B-MoE, and 397B-MoE (post-trained on top of Gemma 4 and Qwen 3.5), achieving state-of-the-art performance among open-source models of comparable size on coding benchmarks such as Terminal-Bench 2.1, SWE-Bench, NL2Repo and OpenClaw.
• Self-Improving Training Framework: Ornith-1.0 employs RL to learn to generate not only solution rollouts, but also the scallfold that drive those rollouts. By jointly optimizing the scaffold and the resulting solution, the model discovers better search trajectories and generates higher-quality solutions.

[!NOTE] These MLXs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

Model Overview

Get started

• Atomic Chat: search AtomicChat/ornith-9b-MLX-4bit and hit Use this model.
• mlx-lm: mlx_lm.generate --model AtomicChat/ornith-9b-MLX-4bit --prompt "Hello" --max-tokens 512
• Server: mlx_lm.server --model AtomicChat/ornith-9b-MLX-4bit --port 8080

Best practices

How these were made

1. Download deepreinforce-ai/Ornith-1.0-9B (original weights).
2. Convert and quantize with mlx_lm.convert on our pipeline.

License