---

license: mit

base_model: khudgins/Ornith-1.0-35B-ThinkingCap

base_model_relation: quantized

tags:

- gguf

- llama.cpp

- ollama

- thinking-cap

- reasoning-efficiency

- code

- moe

language:

- en

pipeline_tag: text-generation

---

Ornith-1.0-35B — Thinking-Cap — GGUF

This card describes the GGUF builds of

khudgins/Ornith-1.0-35B-ThinkingCap —

the ThinkingCap reasoning-efficiency fine-tune of deepreinforce-ai/Ornith-1.0-35B (a 256-expert

Mixture-of-Experts coding model) — 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-1.0-35B-ThinkingCap-Q8_0.gguf | Q8_0 | 35 GB | Recommended — practical build; plan for ~40 GB RAM/VRAM |

| Ornith-1.0-35B-ThinkingCap-f16.gguf | F16 | 65 GB | Full-precision reference |

Lower quants aren't provided here — on the 9B a Q4 of the base couldn't complete the eval, so Q8

is the floor I'd trust for this family.

Usage

Ollama (pulls the GGUF straight from this repo):

ollama run hf.co/khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0 "How many positive integers under 1000 are divisible by neither 5 nor 7?"

llama.cpp:

# pull + run
llama-cli   -hf khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0 -p "your prompt"
# or serve an OpenAI-compatible endpoint
llama-server -hf khudgins/Ornith-1.0-35B-ThinkingCap-GGUF:Q8_0

Fidelity

The tune's accuracy/token numbers (GSM8K 96% @ −24% tokens, HumanEval held, MBPP +2, avg 80.7% →

83.3%) were measured on the adapter via transformers — see the

adapter card.

For the 9B, a matched f16-vs-Q8 Ollama sweep showed quantization was effectively free

(−1.4 pts avg). For this 35B build I ran a coherence check on the Q8 rather than a full N=50

quant sweep — it reasons correctly (clean inclusion-exclusion derivations, correct answers) — so

treat Q8 as the practical build and f16 as the precision reference, with the caveat that the quant

delta here is un-swept rather than measured.

Notes

• No speculative decoding — the Qwen3.5 MTP head is dropped for llama.cpp compatibility.
• Reconverting from the merged HF model? On this MoE the MTP layer count lives in the nested

config.text_config — set text_config.mtp_num_hidden_layers = 0 before converting, or the

converter emits a phantom block (blk.40, block_count 41) and the GGUF fails to load with

missing tensor 'blk.40.attn_norm.weight'. Full recipe in the

tool repo (tools/).

License & provenance

MIT, same chain as the adapter: base deepreinforce-ai/Ornith-1.0-35B (MIT) → post-trained on

Qwen3.5 (Apache-2.0). Method (an independent reproduction of BottleCap AI's ThinkingCap), training,

and full results: see the

adapter model card and the

tool repo.