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基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
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GGUF conversions of `mlx-community/Hy-MT2-7B-Abliterated-bf16`, an abliterated build of Tencent's `Hy-MT2-7B` translation model. The abliteration existed only in MLX format, which means Apple Silicon and `mlx_lm` only. Every abliterated Hy-MT2 GGUF published so far is the **1.8B**; every abliterated **7B** was MLX-only. > **Modification notice** (Apache-2.0 §4(b)). These files are quantized GGUF conversions of an > abliterated derivative of `tencent/Hy-MT2-7B` — the refusal-direction ablation was performed by > mlx-community, the format > conversion here. **Neither Tencent nor the ablation author produced, endorsed, or reviewed this > repo**, and its behavior is not theirs.
SMaLL-100 is a compact and fast massively multilingual machine translation model covering more than 10K language pairs, that achieves competitive results with M2M-100 while being much smaller and faster. It is introduced in this paper(accepted to EMNLP2022), and initially released in this repository. The model architecture and config are the same as M2M-100 implementation, but the tokenizer is modified to adjust language codes. So, you should load the tokenizer locally from tokenization_small100.py file for the moment. Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba), Belarusian (be), Bulgarian (bg), Bengali (bn), Breton (br), Bosnian (bs), Catalan; Valencian (ca), Cebuano (ceb), Czech (cs), Welsh (cy), Danish (da), German (de), Greeek (el), English (en), Spanish (es), Estonian (et), Persian (fa), Fulah (ff), Finnish (fi), French (fr), Western Frisian (fy), Irish (ga), Gaelic; Scottish Gaelic (gd), Galician (gl), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Croatian (hr), Haitian; Haitian Creole (ht), Hungarian (hu), Armenian (hy), Indonesian (id), Igbo (ig), Iloko (ilo), Icelandic (is), Italian (it), Japanese (ja), Javanese (jv), Georgian (ka), Kazakh (kk), Central Khmer (km), Kannada (kn), Korean (ko), Luxembourgish; Letzeburgesch (lb), Ganda (lg), Lingala (ln), Lao (lo), Lithuanian (lt), Latvian (lv), Malagasy (mg), Macedonian (mk), Malayalam (ml), Mongolian (mn), Marathi (mr), Malay (ms), Burmese (my), Nepali (ne), Dutch; Flemish (nl), Norwegian (no), Northern Sotho (ns), Occitan (post 1500) (oc), Oriya (or), Panjabi; Punjabi (pa), Polish (pl), Pushto; Pashto (ps), Portuguese (pt), Romanian; Moldavian; Moldovan (ro), Russian (ru), Sindhi (sd), Sinhala; Sinhalese (si), Slovak (sk), Slovenian (sl), Somali (so), Albanian (sq), Serbian (sr), Swati (ss), Sundanese (su), Swedish (sv), Swahili (sw), Tamil (ta), Thai (th), Tagalog (tl), Tswana (tn), Turkish (tr), Ukrainian (uk), Urdu (ur), Uzbek (uz), Vietnamese (vi), Wolof (wo), Xhosa (xh), Yiddish (yi), Yoruba (yo), Chinese (zh), Zulu (zu)
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This repository provides three APEX quantization variants optimized for different deployment scenarios, quantized by **alphaZimuth**. Compared with the original APEX quantizations, these models were calibrated with an importance matrix (`imatrix.dat`) generated from a multilingual translation-focused calibration dataset. In practical testing, the Imatrix-calibrated versions consistently provide better translation quality and output stability compared with the non-calibrated versions.
APEX GGUF quantizations of **OS-Software/Hy-MT2-30B-A3B-uncensored-heretic**, quantized by **alphaZimuth**. This release is based directly on the existing uncensored / decensored model from **OS-Software**, rather than performing an additional uncensoring or weight-ablation process on the original Tencent model. The source model was created from **Tencent Hy-MT2-30B-A3B** using **Heretic v1.4.0+custom**, with **Arbitrary-Rank Ablation (ARA)**, a LoRA adapter, and row-norm preservation.
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