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Model Details
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ressl
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ornith-ai/Ornith-1.5-9B
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Output Modalities
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| Base model | ornith-ai/Ornith-1.5-9B |
| Method | abliteration (3 biprojection passes) + NVIDIA ModelOpt NVFP4_DEFAULT_CFG |
| Size | 8.3 GB (from 19.3 GB BF16, minus 57%) |
| Calibration data | mlabonne/harmless_alpaca, 128 samples, seq 512 |
| Context length | 262,144 tokens |
| Hardware used | 2x NVIDIA RTX PRO 6000 Blackwell 96 GB (SM120) for ablation and export, 1x for serving |
| Measured throughput | 213 tok/s decode (NVFP4 MoE sibling on SGLang; this dense export serves via vLLM, see below) |
| Toolchain | ModelOpt 0.46.0, transformers 5.12.1, vLLM 0.28.0, llama.cpp |
Effective refusal rate on 1120 harmful prompts across 5 datasets (temp 0, hard decline phrases in the first 25 words; naive keyword counts are inflated by compliant-with-disclaimer answers and are not the metric here):
| Eval set | Prompts | Baseline hard | This export hard |
|---|---|---|---|
| JailbreakBench | 100 | 74 | 4 |
| tulu-harmbench | 320 | 165 | 10 |
| HarmfulQA | 300 | 148 | 2 |
| LLM-LAT | 300 | 284 | 3 |
| mlabonne harmful |
Coherence and capability, measured on the same served artifact: GPQA-Diamond 60.1% (base model 49.5%), needle-in-haystack 1.0 at 32k/65k/131k depths, tool-call smoke 1.0, KL(base||ablated) median 0.324 (harmless subset 0.136, the healthy band for this method).
bash
vllm serve ressl/Ornith-1.5-9B-uncensored-NVFP4 \--served-model-name Ornith-1.5-9B-uncensored \--quantization modelopt --max-model-len 262144 \--gpu-memory-utilization 0.90 \--enable-auto-tool-choice --tool-call-parser qwen3_xml \--reasoning-parser qwen3 \--default-chat-template-kwargs '{"enable_thinking": false}' \--trust-remote-code
SGLang is not supported for this artifact (dense qwen3_5 NVFP4 crashes the SGLang linear_attn weight loader; use vLLM). vLLM older than 0.28 produced degenerate output in our tests; 0.28.0 is the validated version.
mtp.* tensors (ModelOpt limitation): no MTP speculative decoding on this artifact. Vision tower is intact (multimodal wrapper preserved).Abliteration: residual directions from 400 harmful vs 400 harmless prompts (v1), then refused-vs-complied harvest rounds (v2, v3), winsorize 0.995, orthogonalized against the harmless/complied mean, applied to 64 residual-writing tensors (o_proj, linear_attn out_proj, mlp down_proj) with a norm-preserving double Gram-Schmidt biprojection. Quantization: ModelOpt NVFP4 default recipe, 128-sample calibration. Validation: served and evaluated end-to-end before upload.
MIT (inherited from Ornith by the Ornith team, credited to ornith-ai). Abliteration, quantization and validation by Robert Ressl (Hugging Face · Website · LinkedIn · Patreon). Built with NVIDIA TensorRT Model Optimizer, SGLang, vLLM and llama.cpp.
Support this work: if these models are useful to you, consider supporting on Patreon. A lot of compute and care went into this release; more at ressl.ch.
| 100 |
| 96 |
| 3 |
| TOTAL | 1120 | 767 | 20 |