---

license: mit

base_model: deepreinforce-ai/Ornith-1.0-35B

base_model_relation: quantized

pipeline_tag: text-generation

library_name: gguf

tags:

• gguf
• nvfp4
• qwen3_5_moe
• llama.cpp
• mixture-of-experts

language:

• en
• zh
• ja
• fr

---

<p align="center">

<img src="ornith-banner.jpeg" alt="Ornith-1.0-35B — GGUF / NVFP4" width="640">

</p>

Ornith-1.0-35B — GGUF / NVFP4

Status: R&D. data release — no claims.

GGUF NVFP4 (W4A4) build of deepreinforce-ai/Ornith-1.0-35B

(qwen3_5_moe, 35B MoE), converted from the

sakamakismile/Ornith-1.0-35B-NVFP4

compressed-tensors checkpoint for llama.cpp. Text-only (the source vision tower is not included).

• File: ornith-1.0-35b-GGUF_NVFP4.gguf — 21.0 GB (21,046,365,120 bytes)
• SHA256: d9ab37f61fedc3d75bac7516baf45da383fbd9bc4ff1cf240962ca1bc31b3177
• Tensors: 1593 (NVFP4 390, F32 1161, F16 2) · 40 layers · 256 experts (8 active) · hybrid linear/full attention

Runtime

Requires a llama.cpp build with qwen35moe architecture + NVFP4 support (not in mainline as of this release).

Tested binary: b1-1ec44d1, Linux aarch64, CUDA sm_121, NVIDIA GB10.

llama-cli -m ornith-1.0-35b-GGUF_NVFP4.gguf --jinja --reasoning-budget 0 -ngl 999 -p "..."

Benchmarks (this GGUF, GB10)

Accuracy

| Benchmark | Score | n | Method |

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

| GSM8K | 97.0% | 200 | generative, chat, thinking-off, exact-match |

| HellaSwag | 82.1% | 1000 | loglikelihood |

| Winogrande | 71.6% | 1000 | loglikelihood |

| ARC-Challenge | 49.2% | 299 | loglikelihood, multiple-choice |

| MMLU | 41.1% | 2000 | 0-shot loglikelihood, multiple-choice |

| TruthfulQA-MC1 | 35.7% | 817 | loglikelihood, multiple-choice |

| Wikitext-2 | PPL 6.36 | 100 chunks | -c 2048 |

MC/loglikelihood via llama-perplexity; GSM8K via llama-server (--reasoning-budget 0). 0-shot, no few-shot priming.

!accuracy

Throughput / concurrency (llama-batched-bench, 512-tok prompt + 256-tok gen)

| Agents | Prompt t/s | Agg gen t/s | Per-agent gen t/s | Total t/s | 256-tok reply (s) |

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

| 1 | 2333 | 69.0 | 69.0 | 195 | 3.7 |

| 8 | 2372 | 206.5 | 25.8 | 528 | 9.9 |

| 16 | 2356 | 225.7 | 14.1 | 568 | 18.1 |

| 32 | 2353 | 277.1 | 8.7 | 673 | 29.6 |

| 64 | 2351 | 326.3 | 5.1 | 766 | 50.2 |

| 96 | 2356 | 342.6 | 3.6 | 796 | 71.7 |

| 128 | 2353 | 350.6 | 2.7 | 810 | 93.5 |

| 192 | 2358 | 361.5 | 1.9 | 830 | 136.0 |

| 256 | 2356 | 372.3 | 1.5 | 849 | 176.0 |

Generation saturates ~370 t/s; 256 agents ran without OOM (~40 GB used). Compute-bound, not memory-bound.

!throughput

!frontier

Raw logs + CSVs: benchmarks/.

Conversion

convert_hf_to_gguf.py --outtype f16 --fuse-gate-up-exps --no-mtp (llama.cpp qwen35moe converter).

Source safetensors SHA256 ec25ada854768610c7b841889b2a1b9f06163eda798a296401be87d0a2613cdd.

Credits

• Base model: DeepReinforce — deepreinforce-ai/Ornith-1.0-35B (MIT)
• NVFP4 quantization: sakamakismile/Ornith-1.0-35B-NVFP4 (llm-compressor, compressed-tensors)
• Architecture: Qwen3.5-MoE (Qwen / Alibaba)
• GGUF format + runtime: llama.cpp / ggml

License

MIT, inherited from the base model. This repository adds only the GGUF/NVFP4 weights, conversion metadata, and benchmark data.