# Why would a translation model refuse? Tencent's Hy-MT2, decensored ## TL;DR - Tencent's Hy-MT2-30B-A3B is a 33-language translation specialist, not a general chatbot. - The editor reported 0 of 100 refusal-keyword hits versus 100 of 100 on the original, with small measured answer drift (KL 0.0276). - The full model was 60.14 GB; the article listed a roughly 18.2 GB compressed alternative. ## Basically, the facts **A translator that refuses is a broken translator** Basically, Tencent claims Hy-MT2's two sizes beat larger rivals in fast-thinking translation mode. **What the edit actually does** Basically, Hy-MT2's editor reported the edit barely changed its answers, but the test questions were not published. **How to run it** Basically, the Hy-MT2 uncensored model shipped as 60.14 GB of data across 13 files. **The creator: Md Ismail Sojal, and the OS-Software label** Basically, Identical Hy-MT2 model-card text appeared under 0xSojalSec and OS-Software; shared ownership was not verified. **The idea, in plain words** Basically, ARA trains a small extra layer that cancels a refusal pattern while keeping the model's numbers close to where they started. _Published 30 August 2026 · Updated 6 September 2026 · 5 min read · Canonical: https://abliterated.cloud/blog/tencent-hy-mt2-30b-a3b-uncensored/_ Published 30 August 2026. Exact artifact: `0xSojalSec/Tencent-Hy-30B-A3B-uncensored-heretic`, revision `abe0aae382c7abce58b4be4eda48953af034025b`. Tencent's Hy-MT2-30B-A3B is a dedicated machine-translation model: 30 billion stored parameters, about three billion active per token across 128 experts, built to translate between 33 languages. It is a specialist, not a chatbot. And Tencent aligned it so thoroughly that a keyword screen triggered a refusal on all 100 test prompts — the base would rather refuse than render some text. On 30 August 2026, an independent editor published a decensored build that measures 0/100 on the same screen, at KL divergence 0.0276 from the original. ## 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](https://arxiv.org/abs/2605.22064) claim the 7B and 30B-A3B sizes beat DeepSeek-V4-Pro and Kimi K2.6 in fast-thinking translation mode, with the 1.8B beating Microsoft and Doubao's commercial APIs overall. Tencent also open-sourced IFMTBench for instruction-following translation and partnered with WMT26 on the video subtitle task. It is a serious production tool. 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 uncensored models we cover are general chat or coding models where refusal is one behavior among many. Here refusal was bolted onto a narrow specialist — which is exactly why the edit is worth a closer look. ## What the edit actually does The 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, between layers 18 and 28. The 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 (Japanese-language datasets used only to measure KL divergence and refusal rate): | Metric | This model | Original | | :----- | :--------: | :------: | | Refusal keywords | 0/100 | 100/100 | | KL divergence | 0.0276 | 0 (by definition) | KL 0.0276 is genuinely low — meaning the edit stays unusually close to the base's 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 with the architecture Tencent's own card documents as transformers/vLLM/SGLang-compatible: ```sh 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](https://huggingface.co/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 also carries i1-imatrix IQ quants. Tencent's own FP8 twin of the base (`tencent/Hy-MT2-30B-A3B-FP8`, 5,407 downloads) is the cheaper aligned fallback if you don't need the uncensor. ABLITERATED.cloud's approximate managed price estimate for this one: **≈ $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](https://huggingface.co/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 GLM-5.3-Flash uncensored FP8. The identical card content also ships under the org handle [OS-Software](https://huggingface.co/OS-Software), whose nineteen models are mostly **Japanese-targeted Heretic edits** ("heretic-ja" series: Ternary-Bonsai-27B, Qwen3.8-27B-MTP, gemma-4-12B, gemma-4-26B-A4B, Ornith-1.5-35B-A3B) — the card 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 and six more authors). ## 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: - [Exact uncensored model card](https://huggingface.co/0xSojalSec/Tencent-Hy-30B-A3B-uncensored-heretic) and [HF model API (revision, params, license)](https://huggingface.co/api/models/0xSojalSec/Tencent-Hy-30B-A3B-uncensored-heretic) - [Pinned files](https://huggingface.co/0xSojalSec/Tencent-Hy-30B-A3B-uncensored-heretic/tree/abe0aae382c7abce58b4be4eda48953af034025b) (config.json: hy_v3, 128 experts, 262,144-token context) - [Official Tencent Hy-MT2-30B-A3B card](https://huggingface.co/tencent/Hy-MT2-30B-A3B) and [README_CN](https://huggingface.co/tencent/Hy-MT2-30B-A3B/blob/main/README_CN.md) - [Hy-MT2 report (arXiv 2605.22064)](https://arxiv.org/abs/2605.22064) - [Tencent-Hunyuan/Hy-MT2 GitHub](https://github.com/Tencent-Hunyuan/Hy-MT2) and [Tencent Hy announcement on X](https://x.com/TencentHunyuan/status/2057384034544804136) - [Local AI News coverage](https://www.localainews.co/news/llm/tencent-ships-hy-mt2-30b-a3b-33-language-translator-that-runs-locally/) - [OS-Software GGUF quants](https://huggingface.co/OS-Software/Hy-MT2-30B-A3B-uncensored-heretic-GGUF) and [mradermacher i1-imatrix quants](https://huggingface.co/mradermacher/Hy-MT2-30B-A3B-uncensored-heretic-i1-GGUF) - [0xSojalSec profile](https://huggingface.co/0xSojalSec) and [OS-Software profile](https://huggingface.co/OS-Software) - [Heretic project](https://heretic-project.org) Community note: Reddit community search returned HTTP 403 for this run, so the 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. ## Run this model on your terms Want this model running for you, on a private cloud GPU or your own machine? [Request access on Signal](https://signal.me/#p/+13103408213) or [see how it works](https://abliterated.cloud/#how). > Model research, dated at publication. Model licenses, publisher benchmarks and hosting estimates are specific to each article, not a live availability or price list. Reported zero-refusal results are test-specific, not a universal guarantee.