TL;DR: Ornith-1.5-397B with refusal behavior ablated from the weights (1/1120 hard refusals cross-dataset), in NVIDIA NVFP4 experts-only format: 232 GB, serves on 4x RTX PRO 6000 Blackwell.

Same two-pass ablated master as the FP8 sibling repo, quantized with NVIDIA TensorRT Model Optimizer 0.46 (NVFP4, experts only: attention, shared expert, router, embeddings, linear attention and vision tower stay BF16). The practical way to run an uncensored 397B-class model on a single workstation node.

Facts & figures


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

Refusal evaluation

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)

Benchmark results (measured on the FP8 sibling, same ablated master)

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.

Run it with SGLang

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

Companion formats

FP8 (405 GB, drop-in replacement for the official FP8 repo): ressl/Ornith-1.5-397B-uncensored-FP8

Quality & limitations

Will comply with requests a stock model refuses; use responsibly and within your local laws. Intended for security research, red-teaming and creative work.
NVFP4 is a 4-bit weight format; expect a small quality delta vs the FP8 sibling on hard reasoning tasks. The refusal eval above was run on this exact NVFP4 artifact; benchmark suites were run on the FP8 sibling (see its card).
Identity answers reflect the base model's training (inherited from ornith-ai), not an artifact of abliteration or quantization.
Vision tower is included and kept in BF16; only the language decoder's routed experts are NVFP4.

Provenance & reproducibility

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.

License & credits

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.