Ornith 1.0
Collection
DeepReinforce's Ornith 1.0 agentic-coding models, self-quantized to GGUF by Atomic Chat with a per-tensor importance matrix. Runs fully offline. • 10 items • Updated • 6
Ornith 1.0 9B, self-quantized to MLX by Atomic Chat. Built straight from DeepReinforce's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.
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.
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
| Property | Value |
|---|---|
| Base model | deepreinforce-ai/Ornith-1.0-9B |
| Parameters | 0.0B |
| Layers | 32 |
| Context length | 262,144 tokens (256K) |
| Vocabulary | 248,320 |
| Modalities | Text, Image |
| Architecture | Dense decoder, 16 attention heads over 4 KV heads, Qwen3_5ForConditionalGeneration |
| This repo | MLX weights |
Get started
- Atomic Chat: search
AtomicChat/ornith-9b-MLX-8bitand hit Use this model. - mlx-lm:
mlx_lm.generate --model AtomicChat/ornith-9b-MLX-8bit --prompt "Hello" --max-tokens 512 - Server:
mlx_lm.server --model AtomicChat/ornith-9b-MLX-8bit --port 8080
Best practices
| Parameter | Value |
|---|---|
| temperature | 1.0 |
| top_p | 1.0 |
| top_k | 20 |
DeepReinforce's recommended sampling configuration for deepreinforce-ai/Ornith-1.0-9B.
How these were made
- Download
deepreinforce-ai/Ornith-1.0-9B(original weights). - Convert and quantize with
mlx_lm.converton our pipeline.
License
Original model by DeepReinforce, released under the MIT license. Full terms: MIT. Quantized by Atomic Chat.
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Model size
9B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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8-bit
Model tree for AtomicChat/ornith-9b-MLX-8bit
Base model
deepreinforce-ai/Ornith-1.0-9B
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("AtomicChat/ornith-9b-MLX-8bit") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True)