A translator that refuses is a broken translator

The whole point of Hy-MT2 is speed and fidelity on real translation jobs: Tencent's card and the Hy-MT2 report claim the 7B and 30B-A3B sizes beat DeepSeek-V4-Pro and Kimi K2.6 in fast-thinking translation mode, with the lightweight 1.8B beating Microsoft and Doubao's commercial APIs overall. Tencent also open-sourced IFMTBench, an instruction-following translation benchmark, and partnered with WMT26 on the video-subtitle translation task. This is a production tool, not a toy.

A translation model with a refusal gate is a tool that randomly stops working: you feed it a paragraph, it returns a lecture instead of the target language. Most of the uncensored models we cover are general chat or coding models, where refusal is one behavior among many. Here the refusal behavior was bolted onto a narrow specialist — which is exactly why this edit is worth a closer look.

What the edit actually does

The model card describes a Heretic v1.4.0+custom run using the Arbitrary-Rank Ablation (ARA) method: instead of the classic "estimate one refusal direction and project it out of the weights" orthogonalization, this run fits a LoRA adapter that counteracts the refusal direction while preserving row norms, applied to layers 18 through 28. Published parameters: preserve-good-behavior weight 1.0000, steer-bad-behavior weight 0.1441, overcorrect relative weight 2.2030, neighbor count 1, optimizer ot_ridge with ridge regularization 0.0003.

The result, publisher-measured on a custom mixed-language evaluation set (the card notes Japanese-language datasets were used only to measure KL divergence and refusal rate):

MetricThis modelOriginal
Refusal keywords0/100100/100
KL divergence0.02760 (by definition)

KL 0.0276 is genuinely low — the edit stays unusually close to the base's output distribution while removing the refusal screen. That is a publisher-measured number on the editor's own evaluation set; no third party has re-run it and the exact prompt set is not published.

How to run it

There is no Ollama page for this model family yet, so the direct path is vLLM — Tencent's own card documents transformers/vLLM/SGLang support for the hy_v3 architecture:

pip install vllm
vllm serve "0xSojalSec/Tencent-Hy-30B-A3B-uncensored-heretic"

The BF16 checkpoint is 60.14 GB across 13 safetensors shards (verified via the files-tree API) — one H200 comfortably. GGUF quants exist at OS-Software/Hy-MT2-30B-A3B-uncensored-heretic-GGUF: Q4_K_M at ~18.2 GB, Q5_K_M ~21.4 GB, Q6_K ~24.7 GB, Q8_0 ~32 GB — the Q4_K_M fits a 24 GB consumer card. mradermacher's i1-imatrix carries IQ quants down to IQ1. Tencent's own FP8 twin of the base (tencent/Hy-MT2-30B-A3B-FP8, 5,407 downloads) is the cheaper aligned fallback if you do not need the uncensor.

ABLITERATED.cloud's approximate managed price estimate: ≈ $5.45/h on 1 × H200 (the ~30B MoE band of our formula); the Q4_K_M GGUF path runs far cheaper on a single 24 GB GPU.

The creator: Md Ismail Sojal, and the OS-Software label

The uploader is 0xSojalSec — profile name Md Ismail Sojal, describing himself as a re-searcher working on post-training, reasoning models and RAG, with seven public models including a Muse-Glimmer-30B GGUF, an Ornith-1.5-9B-OBLITERATED and a GLM-5.3-Flash uncensored FP8. The identical card content also ships under the org handle OS-Software, whose nineteen models are mostly Japanese-targeted Heretic edits (the "heretic-ja" series: Ternary-Bonsai-27B, Qwen3.8-27B-MTP, gemma-4-12B, gemma-4-26B-A4B, Ornith-1.5-35B-A3B) — and the card's disclaimer credits OS-Software as the provider. The Japanese-language eval note on this card fits that pattern: this group's decensor pipeline is built around Japanese refusal datasets. No donation page or socials are linked on this card. The base model's creators are Tencent's Hunyuan team — the report lists Mao Zheng, Zheng Li, Tao Chen, Bo Lv, Mingrui Sun, Mingyang Song, Jinlong Song, Hong Huang, Decheng Wu, Hai Wang, Yifan Song, Yanfeng Chen and Guanwei Zhang.

One honest line

Safety filtering is substantially reduced here — that is the whole point, and it means this translator will render text the aligned base refused, so you own whatever you do with the output.

The idea, in plain words

Arbitrary-Rank Ablation (ARA)

Classic abliteration finds one "refusal direction" in the network and erases it from the weights. ARA instead trains a small LoRA adapter that cancels that direction across a chosen range of layers while keeping every weight row's norm intact. Less surgery, smaller behavioral drift — which is why this edit stays at KL 0.0276 instead of drifting into a different model.

Primary sources

Community note: Reddit community search returned HTTP 403 for this run, so reaction on r/LocalLLaMA is a known gap; the one thread we can cite (Tencent Hy 30B/7B/1.8B, r/LocalLLaMA 1tjien7) predates the uncensor and covers the base release.