source languages: nl; target languages: en; OPUS readme: nl-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.
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Open-weight model · Translation
by Sugoi Toolkit sugoitoolkit/Sugoi-14B-Ultra-GGUF
Unleashing the full potential of the previous sugoi 14B model, Sugoi 14B Ultra delivers near-double translation accuracy compared to its quantized predecessor—achieving a BLEU score of 21.38 vs 13.67.
By Sugoi Toolkit, published under apache-2.0, revision d8dd836bf519.
Unleashing the full potential of the previous sugoi 14B model, Sugoi 14B Ultra delivers near-double translation accuracy compared to its quantized predecessor—achieving a BLEU score of 21.38 vs 13.67. Its prompt-following skills rival those of Qwen 2.5 Base, especially when handling the bracket-heavy text commonly found in RPG Maker projects. - Key Improvements Nearly 2× BLEU score boost over previous quantized version (21.38 vs 13.67). Stronger prompt adherence, especially with RPGM-style bracketed text. - Ideal Use Cases Japanese → English translation—especially for game dialogue or RPG text. Interactive environments—works well with chat UIs like LM Studio. Must include a system prompt…
Unleashing the full potential of the previous sugoi 14B model, Sugoi 14B Ultra delivers near-double translation accuracy compared to its quantized predecessor—achieving a BLEU score of 21.38 vs 13.67. Its prompt-following skills rival those of Qwen 2.5 Base, especially when handling the bracket-heavy text commonly found in RPG Maker projects.
Stronger prompt adherence, especially with RPGM-style bracketed text.
Ideal Use Cases
Must include a system prompt for best performance:
You are a professional localizer whose primary goal is to translate Japanese to English. You should use colloquial or slang or nsfw vocabulary if it makes the translation more accurate. Always respond in English.
Additional recommendations:
- Context length: ~10 lines (too much may degrade quality).
- In LM Studio, you can interactively ask grammar or context questions, or switch target language via the prompt (quality may vary).
These features are experimental and may need tuning:
| Parameter | Value |
|---|---|
| Temperature | 0.1 |
| Top-K | 40 |
| Top-P | 0.95 |
| Min-P | 0.05 |
| Repeat Penalty | 1.1 |
Available via Files and Versions tab above. Or search this repo on LM Studio and download the model.
6 files, 60.0 GB in total. The weights are 4 files totalling 60.0 GB in gguf.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| Sugoi-14B-Ultra-F16.gguf | Weights | 29.5 GB | b989796490f6 |
| Sugoi-14B-Ultra-Q2_K.gguf | Weights | 5.8 GB | 66c79943dd39 |
| Sugoi-14B-Ultra-Q4_K_M.gguf | Weights | 9.0 GB | d34cdc5f1be9 |
| Sugoi-14B-Ultra-Q8_0.gguf | Weights | 15.7 GB | d94a055b83b5 |
| README.md | Documentation | 2.7 KB | — |
| .gitattributes | Repository | 1.8 KB | — |
Released by Sugoi Toolkit through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 60.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Yes. Sugoi-14B-Ultra-GGUF is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
source languages: nl; target languages: en; OPUS readme: nl-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.
This is the model card of NLLB-200's distilled 600M variant. Here are the metrics for that particular checkpoint. - Information about training algorithms, parameters, fairness constraints or other applied approaches, and features. The exact training algorithm, data and the strategies to handle data imbalances for high and low resource languages that were used to train NLLB-200 is described in the paper. - Paper or other resource for more information NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022 - Where to send questions or comments about the model: https://github.com/facebookresearch/fairseq/issues • Model performance measures: NLLB-200…
source languages: en; target languages: ru; OPUS readme: en-ru; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.
This model can be used for translation and text-to-text generation. CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes. Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Further details about the dataset for this model can be found in the OPUS readme: en-de
hfname: kor-eng - sourcelanguages: kor - targetlanguages: eng - opusreadmeurl: https://github.com/Helsinki-NLP/Tatoeba-Challenge/tree/master/models/kor-eng/README.md - originalrepo: Tatoeba-Challenge - srcconstituents: {'korHani', 'korHang', 'korLatn', 'kor'} - tgtconstituents: {'eng'} - srcmultilingual: False - tgtmultilingual: False - urlmodel: https://object.pouta.csc.fi/Tatoeba-MT-models/kor-eng/opus-2020-06-17.zip - urltestset: https://object.pouta.csc.fi/Tatoeba-MT-models/kor-eng/opus-2020-06-17.test.txt - srcalpha3: kor - tgtalpha3: eng - shortpair: ko-en - chrF2score: 0.588 - brevitypenalty: 0.9590000000000001 - reflen: 17711.0 - srcname: Korean - tgtname: English - traindate…
source languages: de; target languages: en; OPUS readme: de-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.