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 Team Mradermacher mradermacher/Index-Translate-35B-A3B-preview-GGUF
Index-Translate-35B-A3B-preview-GGUF is an open-weight model for translation from Team Mradermacher, released under Apache License 2.0. Its published files total 244.4 GB.
static quants of https://huggingface.co/IndexTeam/Index-Translate-35B-A3B-preview For a convenient overview and download list, visit our model page for this model.
By Team Mradermacher, published under apache-2.0, revision a240c6911455.
static quants of https://huggingface.co/IndexTeam/Index-Translate-35B-A3B-preview For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/Index-Translate-35B-A3B-preview-i1-GGUF If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And here are Artefact2's thoughts on the matter…
static quants of https://huggingface.co/IndexTeam/Index-Translate-35B-A3B-preview
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Index-Translate-35B-A3B-preview-i1-GGUF
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| GGUF | mmproj-Q8_0 | 0.7 | multi-modal supplement |
| GGUF | mmproj-f16 | 1.0 | multi-modal supplement |
| GGUF | Q2_K | 13.3 | |
| GGUF | Q3_K_S | 15.6 | |
| GGUF | Q3_K_M | 17.3 | lower quality |
| GGUF | Q3_K_L | 18.7 | |
| GGUF | IQ4_XS | 19.5 | |
| GGUF | Q4_K_S | 20.5 | fast, recommended |
| GGUF | Q4_K_M | 21.8 | fast, recommended |
| GGUF | Q5_K_S | 24.7 | |
| GGUF | Q5_K_M | 25.4 | |
| GGUF | Q6_K | 29.3 | very good quality |
| GGUF | Q8_0 | 37.9 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.
15 files, 244.4 GB in total. The weights are 13 files totalling 244.4 GB in gguf.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| Index-Translate-35B-A3B-preview.IQ4_XS.gguf | Weights | 19.4 GB | 88066d75a745 |
| Index-Translate-35B-A3B-preview.Q2_K.gguf | Weights | 13.2 GB | 7efd8120fd30 |
| Index-Translate-35B-A3B-preview.Q3_K_L.gguf | Weights | 18.6 GB | 6468c6ed2525 |
| Index-Translate-35B-A3B-preview.Q3_K_M.gguf | Weights | 17.2 GB | 39f84aa0d72a |
| Index-Translate-35B-A3B-preview.Q3_K_S.gguf | Weights | 15.5 GB | 631ea809eb51 |
| Index-Translate-35B-A3B-preview.Q4_K_M.gguf | Weights | 21.7 GB | 748d821c2e15 |
| Index-Translate-35B-A3B-preview.Q4_K_S.gguf | Weights | 20.4 GB | 7897cfda4d53 |
| Index-Translate-35B-A3B-preview.Q5_K_M.gguf | Weights | 25.3 GB | 273bfd2c952d |
| Index-Translate-35B-A3B-preview.Q5_K_S.gguf | Weights | 24.6 GB | 11f0c62c058c |
| Index-Translate-35B-A3B-preview.Q6_K.gguf | Weights | 29.2 GB | 251b0d82d209 |
| Index-Translate-35B-A3B-preview.Q8_0.gguf | Weights | 37.8 GB | 70eea05d95d5 |
| Index-Translate-35B-A3B-preview.mmproj-Q8_0.gguf | Weights | 614.2 MB | 3719c3dce431 |
| Index-Translate-35B-A3B-preview.mmproj-f16.gguf | Weights | 899.3 MB | 307725b513c0 |
| README.md | Documentation | 4.2 KB | — |
| .gitattributes | Repository | 2.6 KB | — |
Released by Team Mradermacher through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 244.4 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Yes. Index-Translate-35B-A3B-preview-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.