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justsumguy/Realtime-Venus-Omni-heretic-ARA
Realtime-Venus-Omni-heretic-ARA is a any-to-any model from justsumguy. Use it for the any-to-any task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
The Realtime-Venus-Omni checkpoint from inclusionAI/Realtime-Venus (itself a fine-tune of openbmb/MiniCPM-o-45), with refusal behaviour reduced by abliteration of the language tower. BF16, same file layout and remote…
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From the Hugging Face model README
The Realtime-Venus-Omni checkpoint from inclusionAI/Realtime-Venus (itself a fine-tune of openbmb/MiniCPM-o-4_5), with refusal behaviour reduced by abliteration of the language tower. BF16, same file layout and remote code as upstream. An NVFP4 build is at justsumguy/Realtime-Venus-Omni-heretic-ARA-NVFP4.
Only the language tower (llm.*, Qwen3-8B shape, 36 layers). The vision tower, audio tower,
resampler, audio projection, TTS talker and token2wav assets are upstream's, unmodified.
self_attn.o_proj and mlp.down_proj in all 36 layers.The upstream sample media (assets/sample_1_real.mp4, assets/speech_in.mp4) are not included.
ara branch at edc3b12 (Arbitrary-Rank Ablation).You are a helpful assistant.mlabonne/harmful_behaviors train[:400] and mlabonne/harmless_alpaca train[:400].Selected trial: 83.
| Parameter | Value |
|---|---|
| layers | 17–31 |
| preserve_good_behavior_weight | 0.1400 |
| steer_bad_behavior_weight | 0.1687 |
| overcorrect_relative_weight | 0.4085 |
| neighbor_count | 15 |
| Metric | Refusals (of 100) | KL divergence |
|---|---|---|
| Base model | 98 | – |
| Study (trial 83) | 2 | 0.0696 |
| Recheck (adapter re-fit for export) | 4 | 0.2494 |
The ARA-LoRA re-fit is not bit-reproducible, so the recheck numbers are the ones that describe these weights.
No healing step (fine-tuning after ablation) was applied. Post-abliteration stability needs further testing. In live full-duplex sessions, long repetition loops were observed on ablated builds and not on the unablated model, over a small number of runs.
Same as upstream: AutoModel.from_pretrained(path, trust_remote_code=True, torch_dtype=torch.bfloat16),
with the pinned requirements in requirements.txt. BF16 weights are 18.7 GB and do not fit a
16 GB card. Use the NVFP4 build, or bitsandbytes NF4 on llm.* only (about 14.8 GB in a duplex session).
Apache-2.0, as upstream. See LICENSE and NOTICE. The modification is the abliteration of the language tower described above.