source languages: nl; target languages: en; OPUS readme: nl-en; dataset: opus; model: transformer-align; pre-processing: normalization + SentencePiece.
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Index-Nailong-9B-GGUF is an open-weight model for translation from Index Team, released under Apache License 2.0. Its published files total 81.4 GB.
Official GGUF conversion of IndexTeam/Index-Nailong-9B, part of the Index-Translate multilingual translation model family (150 languages, terminology/format-constrained translation, controlled dubbing translation, long-document translation).
By Index Team, published under apache-2.0, revision b77c686a6063.
Official GGUF conversion of IndexTeam/Index-Nailong-9B, part of the Index-Translate multilingual translation model family (150 languages, terminology/format-constrained translation, controlled dubbing translation, long-document translation). Converted with llama.cpp (master, 2026-10); static post-training quantization, all bit-widths in this single repository. (sorted by size) Translation prompt format (greedy decoding, temperature=0 recommended; use the chat template with enablethinking: false): Beyond plain translation, the models follow the instTrans constrained-translation format. The official client wraps requests into the canonical structure 【源文】 + numbered 1. 【硬性要求】 + 2. 【注意】 +…
Official GGUF conversion of IndexTeam/Index-Nailong-9B, part of the Index-Translate multilingual translation model family (150 languages, terminology/format-constrained translation, controlled dubbing translation, long-document translation).
Converted with llama.cpp (master, 2026-10); static post-training quantization, all bit-widths in this single repository.
(sorted by size)
| File | Size | Notes |
|---|---|---|
| Q2_K | 3.83 GB | 2-bit, significant quality loss |
| Q3_K_S | 4.26 GB | 3-bit, noticeable quality loss |
| Q3_K_M | 4.62 GB | 3-bit, noticeable quality loss |
| Q3_K_L | 4.93 GB | 3-bit, noticeable quality loss |
| IQ4_XS | 5.23 GB | 4-bit I-quant, small |
| Q4_K_S | 5.35 GB | 4-bit, slightly smaller/faster |
| Q4_K_M | 5.63 GB | 4-bit, good balance, recommended |
| Q5_K_S | 6.31 GB | 5-bit, low quality loss |
| Q5_K_M | 6.47 GB | 5-bit, low quality loss |
| Q6_K | 7.36 GB | 6-bit, very low quality loss |
| Q8_0 | 9.53 GB | 8-bit, near-lossless |
| f16 | 17.92 GB | 16-bit, lossless conversion baseline |
# Run the recommended Q4_K_M directly:
llama serve -hf IndexTeam/Index-Nailong-9B-GGUF:Q4_K_M
llama cli -hf IndexTeam/Index-Nailong-9B-GGUF:Q4_K_M
Translation prompt format (greedy decoding, temperature=0 recommended; use the chat template with enable_thinking: false):
请将以下文本翻译为{{target-language}},直接输出翻译结果,不要进行任何解释。
{{source-text}}
# llama.cpp: run the recommended Q4_K_M with the chat template and thinking disabled
llama-cli -hf IndexTeam/Index-Nailong-9B-GGUF:Q4_K_M --jinja --chat-template-kwargs '{"enable_thinking":false}' --temp 0
Beyond plain translation, the models follow the instTrans constrained-translation format. The official client wraps requests into the canonical structure 【源文】<text> + numbered 1. 【硬性要求】<hard constraints> + 2. 【注意】<soft constraints> + suffix instructions:
碳纤维:carbon fiber, 抗裂缝:crack resistance), and format/structure preservation for JSON/CSV/code/placeholders.Full prompt reference: github.com/bilibili/Index-Translate (Instruction Following section, docs/prompts.md, inference/llm/cases/).
See the base model card for the full instTrans constrained-translation format and serving presets.
Before release, each quantization tier was validated on GPU (NVIDIA A100) against the F16 conversion: per-token KL divergence / RMS delta-p via llama-perplexity, plus greedy-generation spot checks against the original BF16 weights (transformers reference). Generation outputs of Q4_K_M matched the reference almost verbatim.
Converted and published by the Index team, 2026-10-03.
15 files, 81.4 GB in total. The weights are 12 files totalling 81.4 GB in gguf.
| File | Type | Size | SHA-256 |
|---|---|---|---|
| Index-Nailong-9B.IQ4_XS.gguf | Weights | 5.2 GB | af47a4d4bdce |
| Index-Nailong-9B.Q2_K.gguf | Weights | 3.8 GB | 5f4c901094c0 |
| Index-Nailong-9B.Q3_K_L.gguf | Weights | 4.9 GB | 5d9d72da8224 |
| Index-Nailong-9B.Q3_K_M.gguf | Weights | 4.6 GB | 2ee983c3693b |
| Index-Nailong-9B.Q3_K_S.gguf | Weights | 4.3 GB | 45c7102e4831 |
| Index-Nailong-9B.Q4_K_M.gguf | Weights | 5.6 GB | fb5c7716147c |
| Index-Nailong-9B.Q4_K_S.gguf | Weights | 5.4 GB | b28524320525 |
| Index-Nailong-9B.Q5_K_M.gguf | Weights | 6.5 GB | 30fd1bc9711c |
| Index-Nailong-9B.Q5_K_S.gguf | Weights | 6.3 GB | 99a858cbcae4 |
| Index-Nailong-9B.Q6_K.gguf | Weights | 7.4 GB | 02ec1393bb42 |
| Index-Nailong-9B.Q8_0.gguf | Weights | 9.5 GB | 1b810d43b08f |
| Index-Nailong-9B.f16.gguf | Weights | 17.9 GB | d5bf4ce5263b |
| README.md | Documentation | 5.2 KB | — |
| SHA256SUMS.txt | Other | 1.1 KB | — |
| .gitattributes | Repository | 2.3 KB | — |
Released by Index Team through its official repository on Hugging Face. Read the license.
| Precision | Weights in memory |
|---|---|
| As published | 81.4 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Yes. Index-Nailong-9B-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.