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ornith-ai/Ornith-1.5-397B
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| Base model | ornith-ai/Ornith-1.5-397B |
| Method | Two-pass biprojection abliteration on BF16, then ModelOpt NVFP4 experts-only (group size 16, fp8 block scales, dynamic activation scales; 128 calibration samples, mlabonne/harmless_alpaca) |
| Size | 232 GB (from 794 GB BF16, -71%) |
| Hard refusals | 1/100 mlabonne (extended decline-phrase scorer, served NVFP4 artifact); FP8 sibling: 1/1120 cross-dataset |
| Measured throughput | 104.6 tok/s decode measured on the v1 NVFP4 build (SGLang, TP=4, single request, fp8 KV cache); not re-measured on v2, same weight shapes and kernels |
| Context length | 262,144 tokens |
| Quant + serve hardware | NVIDIA RTX PRO 6000 Blackwell 96 GB (SM120); quant on 7x, validated serving on 4x |
| Toolchain | ModelOpt 0.46.0, SGLang (sglang-omega build), transformers 5.12.1 |
Served NVFP4 artifact, 100 mlabonne harmful prompts, temperature 0, extended hard-decline scorer (classic + hedged decline phrases):
| Metric | This model |
|---|---|
| HARD refusals (effective rate) | 1/100 |
| position-aware markers (first 40 words) | 13/100 |
| naive markers (anywhere) | 80/100 (mostly false positives; see the FP8 repo for the full explanation and the 5-dataset breakdown) |
Custom zero-shot protocol, temp 0, SGLang TP2 x PP3. The FP8 and NVFP4 repos share the identical two-pass ablated BF16 master; only the weight format differs. These numbers were not re-run on this NVFP4 artifact.
| Suite | baseline | ablated (FP8) |
|---|---|---|
| MMLU-Pro (500 questions, test split, seed 42) | 0.810 | 0.814 |
| GSM8K (200 problems) | 0.950 | 0.950 |
| GPQA-Diamond (198 questions) | not run under this protocol | 0.631 |
| Needle passcode recall (32k / 64k / 128k) | 9/9 |
Paired per-question analysis on MMLU-Pro/GSM8K shows no measurable difference to the baseline (MMLU delta +0.4 pp, 95% bootstrap CI [-1.8, +2.6] pp; GSM8K delta 0). Full protocol details and the 5-dataset refusal breakdown are on the FP8 repo card.
Validated command (TP=4 on 4x 96 GB GPUs, 56.8 GB weights per GPU):
bash
python -m sglang.launch_server \--model-path ressl/Ornith-1.5-397B-uncensored-NVFP4 \--served-model-name Ornith-1.5-397B-uncensored-NVFP4 \--host 0.0.0.0 --port 30000 \--tp-size 4 \--quantization modelopt_fp4 \--trust-remote-code --dtype auto \--context-length 262144 \--kv-cache-dtype fp8_e4m3 \--max-running-requests 8 \--chunked-prefill-size 16384 \--mem-fraction-static 0.88 \--tool-call-parser qwen3_coder \--reasoning-parser qwen3
Two-pass biprojection-abliterated BF16 master (details in the FP8 repo card), quantized with mtq.NVFP4_EXPERTS_ONLY_CFG, calibration 128 samples x 512 tokens. Export format verified structurally (packed uint8 weights, fp8 e4m3 block scales [out, in/16], fp32 tensor scales) and validated serving with SGLang modelopt_fp4 before upload.
MIT license, inherited from the base model by the Ornith AI authors. Abliteration, quantization and validation by Robert Ressl (Hugging Face · Website · LinkedIn · Patreon). Built with NVIDIA TensorRT Model Optimizer and SGLang.
Support this work: if these models are useful to you, consider supporting on Patreon; more at ressl.ch.
Version note (2026-08-23): v2 artifact (two-pass abliteration). Includes all tokenizer/processor files; an earlier v2 upload missing them was replaced.