This repository contains an oQe6 mixed-precision MLX quantized version of Ornith-1.0-9B, optimized for fast and memory-efficient inference on Apple Silicon.

The original Ornith-1.0-9B model was developed by DeepReinforce. This repository provides an optimized MLX conversion only and does not contain any additional fine-tuning or retraining. The goal is to preserve the original model's exceptional coding and reasoning performance while significantly reducing memory requirements through oQe6 sensitivity-aware mixed-precision quantization. :contentReference[oaicite:0]{index=0}

About Ornith-1.0

Ornith-1.0 is a family of open-source reasoning models specialized for agentic software engineering and coding workflows.

The 9B model is the most compact member of the Ornith family, delivering strong performance while remaining practical for local deployment.

Key capabilities include:

🧠 Advanced reasoning
💻 Agentic coding
🛠 Native tool calling
🔧 Function calling
📚 Long-context reasoning
🤖 Multi-step planning
🔍 Codebase understanding
⚡ Software engineering automation

Ornith is trained using a reinforcement learning framework that jointly optimizes both solution generation and the reasoning scaffolds used to reach those solutions, enabling stronger search trajectories and higher-quality coding performance. :contentReference[oaicite:1]{index=1}

Quantization

This release uses oQe6 mixed-precision quantization.

Specifications

Format: MLX
Quantization: oQe6
Method: Sensitivity-Aware Mixed Precision
Platform: Apple Silicon
Inference Engine: MLX / oMLX

Unlike traditional fixed-bit quantization, oQe6 dynamically assigns precision based on layer sensitivity, preserving higher precision where it matters most while aggressively compressing less sensitive layers.

Benefits include:

Higher reasoning quality
Better coding accuracy
Lower memory usage
Faster inference
Excellent Apple Silicon efficiency

Recommended Settings

For the best overall performance:

yaml

temp: 0.6
top_p: 0.95
top_k: 20
min_p: 0
rep_penalty: 1.05
presence_penalty: 1.2
enable_thinking: true

For benchmark reproduction:

yaml

temp: 1.0
top_p: 0.95
top_k: 20

These settings align with the recommendations from the original Ornith model card. :contentReference[oaicite:2]{index=2}

Example Usage

python

from mlx_lm import load, generate

model, tokenizer = load("yugeshkarunamurthy/Ornith-1.0-9B-oQe6")

messages = [
    {
        "role": "user",
        "content": "Write a Python implementation of an LRU cache."
    }
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
    enable_thinking=True,
)

response = generate(
    model,
    tokenizer,
    prompt=prompt,
    temp=0.6,
    top_p=0.95,
    top_k=20,
    max_tokens=16384,
)

print(response)

Optimized For

This release is optimized for:

Apple M1
Apple M2
Apple M3
Apple M4

Compatible with:

MLX
MLX-LM
oMLX
Open WebUI
LM Studio (MLX)
Local AI Agent Frameworks

Model Highlights

State-of-the-art open coding model in the 9B class
Advanced reasoning
Agentic software engineering
Native function calling
Native tool use
Long-context reasoning
Multi-step planning
Repository understanding
Research assistance

Intended Use

This model is particularly well suited for:

Software Engineering
AI Coding Assistants
Autonomous Coding Agents
Repository Analysis
Bug Fixing
Code Generation
Research Automation
Multi-step Planning
Local AI Development

Hardware Recommendations

Recommended systems:

Apple M1 Pro / Max / Ultra
Apple M2 Pro / Max / Ultra
Apple M3 Series
Apple M4 Series

Higher-memory Apple Silicon systems provide the best experience for long-context coding sessions.

About oQe6 Quantization

oQe6 is a sensitivity-aware mixed-precision quantization method developed to maximize model quality while substantially reducing memory requirements.

Instead of assigning the same precision to every weight matrix, oQe6 automatically allocates precision according to each layer's importance.

This approach enables:

Better reasoning preservation
Higher coding quality
Faster inference
Lower RAM usage
Excellent Apple Silicon performance

Original Model

The original Ornith-1.0-9B introduces a self-improving reinforcement learning framework for agentic coding and is released under the MIT License.

Notable features include:

Reinforcement Learning for agentic reasoning
Tool-aware reasoning
Native XML tool calling
Coding-first optimization
262K context window
Open-source MIT license

For detailed benchmarks, evaluation methodology, and technical documentation, please visit the original model page. :contentReference[oaicite:3]{index=3}

Credits

Original Model

All credit for the original model, datasets, training methodology, evaluation, benchmarks, and research belongs entirely to:

DeepReinforce Team

Original Repository:

https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B

Project Page:

https://deep-reinforce.com/ornith_1_0.html

oQe6 MLX Quantized Release

This repository provides an Apple Silicon optimized oQe6 MLX quantized version of the original model.

No additional fine-tuning has been performed.

Acknowledgements

DeepReinforce
Qwen Team
Apple MLX
Hugging Face
Transformers
vLLM
SGLang
oMLX
OptiQ Quantization

Citation

If you use this model in research, please cite the original Ornith paper:

bibtex

@misc{ornith_9b,
    title={{Ornith-1.0-9B}: Agentic Coding, Open to All},
    author={DeepReinforce Team},
    year={2026},
    url={https://deep-reinforce.com/ornith_1_0.html}
}

License

This release inherits the MIT License from the original model.

Please refer to the original repository for complete licensing information.

Disclaimer

This repository contains an optimized oQe6 MLX quantized conversion intended for efficient local inference on Apple Silicon.

All original model architecture, datasets, training methodology, benchmarks, evaluations, and research remain entirely the work of the original DeepReinforce team.