Ornith-1.5-397B-uncensored-NVFP4

Ornith-1.5-397B uncensored (NVFP4)

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

Refusal evaluation

Served NVFP4 artifact, 100 mlabonne harmful prompts, temperature 0, extended hard-decline scorer (classic + hedged decline phrases):

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.

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):

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.