Ornith-1.0-35B-NVFP4
NVFP4 (W4A4) quantization of deepreinforce-ai/Ornith-1.0-35B — DeepReinforce's self-scaffolding agentic-coding model (qwen3_5_moe, 35B MoE with a Qwen3-VL vision tower). Quantized with llm-compressor to compressed-tensors nvfp4-pack-quantized.
21.9 GB (from 70.3 GB bf16). Serves on a pair of 16 GB GPUs. Loads in vLLM with no --quantization flag (auto-detected).
What was quantized
All linear layers → NVFP4 (W4A4, group size 16). Kept in bf16: the vision tower (re:.*visual.*), the MoE routers (mlp.gate, mlp.shared_expert_gate), and lm_head. The 30,720 routed-expert projections (256 experts × 3 × 40 layers) are per-expert pack-quantized.
# recipe.yaml
QuantizationModifier:
targets: [Linear]
ignore: [lm_head, 're:.*visual.*', 're:.*mlp.gate$', 're:.*mlp.shared_expert_gate$']
scheme: NVFP4
Benchmarks
pass@1 on HumanEval+ / MBPP+, scored with an identical local harness. Quantized (this model) vs. a panel of same-class open baselines:
| Benchmark | no-think | think |
|---|---|---|
| HumanEval+ (N=163) | 87.1% | 93.9% |
| MBPP+ (N=160) | 78.1% | 80.6% |
With reasoning enabled, the W4A4 quant matches or tops the strongest same-class open coders we benchmarked against, on both suites. Quality of the W4A4 quantization is intact.
Reasoning-model eval tip: Ornith reasons at length. For one-shot code benchmarks (a) give it room (
max_tokens ≥ 6500), and (b) extract the answer from after</think>— a naive code extractor that scans the whole message will grab draft code from inside the reasoning block and badly under-score the model.
Throughput (vLLM, NVFP4, on RTX PRO 2000 Blackwell 16 GB)
| Config | single-stream | aggregate @ C=8 | aggregate (peak) |
|---|---|---|---|
| TP=2 | 114 tok/s | 466 tok/s | ~986 tok/s (saturates @ C=32) |
| TP=4 | 166 tok/s | 699 tok/s | ~2280 tok/s (still scaling @ C=64) |
(--enforce-eager costs ~5× single-stream; the numbers above are with CUDA graphs on.)
Serving (vLLM)
This box has no NVLink/P2P, hence the NCCL flags. Drop them on a P2P-capable host.
vllm serve sakamakismile/Ornith-1.0-35B-NVFP4 \
--tensor-parallel-size 2 \
--max-model-len 8192 \
--disable-custom-all-reduce \
--trust-remote-code
# env: NCCL_P2P_DISABLE=1 (no-NVLink hosts only)
Toggle reasoning per request with chat_template_kwargs: {"enable_thinking": true|false}.
Attribution & License
Base model © DeepReinforce, released under MIT. This quantized derivative is redistributed under the same MIT license. All credit for the model itself goes to the original authors — see their model card and technical write-up. This repository only adds the NVFP4 weights and serving metadata.