Heretic's own evaluation: refusals are Heretic's keyword rule over 100 held-out prompts from mlabonne/harmful_behaviors; KL divergence is first-token KL against the original model over 100 prompts from mlabonne/harmless_alpaca. Thinking off. It is not a safety evaluation and says nothing about what the model will or will not produce.
Use
Load exactly as the original — see NVIDIA's model card for transformers / vLLM / SGLang serving, the reasoning parser and recommended sampling. Everything except the edited weights is unchanged, including trust_remote_code requirements.
Thinking: on by default in the template; pass chat_template_kwargs: {"enable_thinking": false} per request to turn it off.
For llama.cpp, use the GGUF repo (vision via mmproj-*.gguf, MTP via mtp-*.gguf).
How it was made
Search. Heretic's ARA modifier (rank-50 LoRA on the residual-stream writers, fitted per layer with L-BFGS) with Optuna's TPE sampler co-minimizing refusals and KL divergence, 260 trials, run on Apple Silicon through an MLX backend for Heretic against an 8-bit MLX copy of the text model. The adapters stay in fp32 on top of the quantized weights, matching PEFT on torch.
Pick. Trial 264 — the parameters above (end_layer_index is exclusive, so layers 41–53). Before export the trial was restored from its parameters and re-scored; it reproduced its stored 6/100 and 0.1092 exactly.
Merge.W + B·A computed in fp32 against the original BF16 weights (not the 8-bit search copy) and rounded to BF16 once.
Graft. The 19 merged tensors were written into NVIDIA's Omni checkpoint under language_model.*. Checked before writing that Omni's originals equal the text weights byte for byte, and after writing that the grafted tensors equal the merge; every other tensor and file is copied unchanged.
Checked after the build: the vision tower still reads images correctly, and the MTP head still drafts (53% of draft tokens accepted on a llama.cpp Q8_0).
Responsible use
The edit lowers refusals. You decide how to use the model and you answer for it under the laws that apply to you. Intended uses: research, evaluation, red-teaming, creative writing and private deployment. Do not use it to produce illegal content or to target people, or in a public service without your own filtering and moderation.