---

license: mit

base_model: khudgins/Ornith-1.0-9B-ThinkingCap

base_model_relation: quantized

tags:

- gguf

- llama.cpp

- ollama

- thinking-cap

- reasoning-efficiency

- code

language:

- en

pipeline_tag: text-generation

---

Ornith-1.0-9B — Thinking-Cap — GGUF

This card describes the GGUF builds of

khudgins/Ornith-1.0-9B-ThinkingCap —

the ThinkingCap reasoning-efficiency fine-tune of deepreinforce-ai/Ornith-1.0-9B — with the LoRA

merged into the base and converted for llama.cpp / Ollama. Text-only (the base's vision tower is

dropped in conversion).

Files

| File | Quant | Size | Use |

|---|---|---|---|

| Ornith-1.0-9B-ThinkingCap-Q8_0.gguf | Q8_0 | 9.5 GB | Recommended daily driver — ~1.4 pts avg below f16, half the memory |

| Ornith-1.0-9B-ThinkingCap-f16.gguf | F16 | 17.9 GB | Publish-precision reference |

Quantization is effectively free here (see the fidelity table below). Lower quants are not

recommended — a Q4 of the base failed to complete the eval at all.

Usage

Ollama (pulls the GGUF straight from this repo):

ollama run hf.co/khudgins/Ornith-1.0-9B-ThinkingCap-GGUF:Q8_0 "A store had 48 apples, sold 3/4, then got 30 more. How many now?"

llama.cpp:

# pull + run
llama-cli   -hf khudgins/Ornith-1.0-9B-ThinkingCap-GGUF:Q8_0 -p "your prompt"
# or serve an OpenAI-compatible endpoint
llama-server -hf khudgins/Ornith-1.0-9B-ThinkingCap-GGUF:Q8_0

Quantization fidelity

Benchmarked via Ollama (one harness for f16 and Q8, isolating the quant effect):

| Format | Size | GSM8K | HumanEval | MBPP | avg |

|---|---|---|---|---|---|

| f16 | 17.9 GB | 91.3% | 90.9% | 77.0% | 86.4% |

| Q8_0 | 9.5 GB | 90.0% | 89.0% | 76.0% | 85.0% |

Q8 lands within 1–3 problems of f16 on every benchmark with identical token behavior.

Notes

• No speculative decoding — the Qwen3.5 MTP head is dropped for llama.cpp compatibility.
• Reconverting from the merged HF model? Set mtp_num_hidden_layers=0 in config.json first,

or the converter counts a phantom 33rd block (blk.32) and the GGUF fails to load. (Full write-up

in the tool repo.)

License & provenance

MIT, same chain as the adapter: base deepreinforce-ai/Ornith-1.0-9B (MIT) → post-trained on

Qwen3.5 (Apache-2.0). Method, training, and full results: see the

adapter model card.