---

language:

• en

license: mit

library_name: gguf

tags:

• text-generation
• gsq
• rco
• llama.cpp
• quantized
• reasoning
• ornith
• mixed-precision
• mixed-prediction
• mixed-prediciton

base_model: ornith-ai/Ornith-1.5-9B-GGUF

base_model_relation: quantized

pipeline_tag: text-generation

---


Ornith-1.5-9B GSQ-RCO GGUF

Mixed-precision GSQ-RCO GGUF quantizations of ornith-ai/Ornith-1.5-9B, optimized for efficient, high-fidelity local inference in llama.cpp.

Benchmarks

!Ornith-1.5-9B GSQ-RCO Benchmark Comparison

Why GSQ-RCO?

Standard quantization severely degrades hybrid SSM-Transformer models (e.g. stock IQ3_M perplexity drops to ~75.2).

GSQ-RCO solves this through two targeted techniques:

• RCO: Keeps all 48 critical SSM parameters in unquantized BF16.
• GSQ: Optimizes per-layer precision guided by Hessian sensitivity calibration.

GGUF Files

| File | bpw | Size |

| :--- | :---: | :---: |

| Ornith-1.5-9B-GSQ-RCO-IQ2_XS.gguf | 2.50 | 2.80 GB |

| Ornith-1.5-9B-GSQ-RCO-IQ2_S.gguf | 2.75 | 3.08 GB |

| Ornith-1.5-9B-GSQ-RCO-IQ3_XXS.gguf | 3.00 | 3.36 GB |

| Ornith-1.5-9B-GSQ-RCO-IQ3_XS.gguf | 3.25 | 3.64 GB |

| Ornith-1.5-9B-GSQ-RCO-IQ3_S.gguf | 3.50 | 3.92 GB |

| Ornith-1.5-9B-GSQ-RCO-IQ3_M.gguf (Recommended) | 3.75 | 4.20 GB |

Each quantization also ships an optional -mtp build that carries the model's Multi-Token Prediction head (layer 32) for speculative decoding in supported runtimes.

Attribution & License

• Distributed under the MIT License, matching upstream ornith-ai/Ornith-1.5-9B.
• Reference GGUF release: ornith-ai/Ornith-1.5-9B-GGUF.