Index-Translate-9B is the 9B-parameter multilingual text model of the Index-Translate family, built on Qwen3.5. It supports text translation across 150 languages and follows translation instructions involving terminology, formatting, style, structure, context, and output length.
mmproj).{variable}, [123456]).Language coverage is documented in the technical report's language inventory. The speech and long-document models have their own packaged interfaces and language coverage.
The technical report describes a shared multilingual mid-training recipe, followed by task-specific post-training:
The evaluated 2B and 9B models combine the general, instruction, and meme specialists with weights 0.8 / 0.1 / 0.1, then receive targeted MOPD. Reported training-stage ablations use separate configurations; they are not the final model scores below.
Results below reproduce the latest technical report. Comparisons apply to the report's evaluation data and settings, and metric scales differ. Higher is better except for off-target rates. These are individual benchmark scores, separate from the demo's normalized radar categories.
The tables compare this checkpoint with the other Index-Translate sizes, Hy-MT2-7B, TranslateGemma-12B, and the Qwen3.5-9B base at a similar scale, and larger translation models and API baselines.
WMT26 Judge uses a 0–100 scale; the other columns use 0–1. instTrans quality and IFscore measure translation quality and instruction adherence separately. IFMTBench reports reference-based XCOMET-XXL and instruction adherence. Vertical is the equal-weight mean of the five domain scores below. North-Small-Translate is the 218B-A25B model; TranslateGemma is translategemma-12b-it.
| Model | FLORES COMET-22 | WMT24++ COMET-22 | WMT26 Judge | instTrans Quality | instTrans IFscore | IFMTBench XCOMET-XXL | IFMTBench IFscore | Vertical mean | MEME |
|---|---|---|---|---|---|---|---|---|---|
| Index-Translate-35B-A3B (preview) | 0.8794 | 0.8586 | 76.76 | 0.6901 | 0.8336 | 0.7926 | 0.8991 | 0.8438 | 0.7405 |
| Index-Translate-9B | 0.8789 | 0.8601 | 75.35 | 0.6771 | 0.8209 | 0.7957 | 0.8760 | 0.8451 | 0.7387 |
| Index-Translate-2B | 0.8655 | 0.8489 | 60.26 | 0.5391 | 0.7569 | 0.7712 | 0.7584 | 0.8377 | 0.6443 |
| Hy-MT2-1.8B | 0.8522 | 0.8401 | 49.35 | 0.3181 | 0.4932 | 0.7493 | 0.7161 | 0.8314 | 0.3643 |
| Hy-MT2-7B | 0.8747 | 0.8593 | 60.51 | 0.5143 | 0.6079 | 0.8049 | 0.8741 | 0.8335 | 0.5139 |
| Hy-MT2-30B-A3B | 0.8787 | 0.8624 | 66.81 | 0.5725 | 0.6415 | 0.8177 | 0.9029 |
FLORES_minor_pair contains 104,000 inputs across 1,040 directions among 62 languages. instTrans_minor contains 2,793 instruction-translation tasks from Chinese or English into low-resource languages. Off-target is the percentage of outputs in a language other than the requested target; lower is better. The other scores use a 0–1 scale.
| Model | FLORES_minor_pair COMET-22 ↑ | FLORES_minor_pair XCOMET-XXL ↑ | FLORES_minor_pair off-target ↓ | instTrans_minor Quality ↑ | instTrans_minor IFscore ↑ | instTrans_minor off-target ↓ |
|---|---|---|---|---|---|---|
| Index-Translate-35B-A3B (preview) | 0.8168 | 0.7164 | 2.4% | 0.5151 | 0.7715 | 4.05% |
| Index-Translate-9B | 0.7992 | 0.6805 | 4.0% | 0.5222 | 0.7725 | 3.47% |
| Index-Translate-2B | 0.7377 | 0.4817 | 4.2% | 0.3050 | 0.6586 | 3.97% |
| Hy-MT2-1.8B | 0.3163 | 0.2014 | 54.2% | 0.0412 | 0.1313 | 65.27% |
| Hy-MT2-7B | 0.4626 | 0.3334 | 35.7% | 0.1121 | 0.2405 | 45.40% |
| Hy-MT2-30B-A3B | 0.6746 | 0.5359 | 14.5% | 0.2246 | 0.4449 | 15.47% |
| TranslateGemma-12B | 0.8021 | 0.6217 | 1.7% | 0.2412 | 0.2682 | 5.37% |
| North-Small-Translate (218B-A25B) | 0.6855 | 0.4919 | 13.2% | 0.2436 | 0.2902 | 26.89% |
| Qwen3.5-2B (base) | 0.3716 | 0.1852 |
COMET-22 is used for OPUS-Books, WMT Biomedical, MTNT, and MuST-Cinema; GuoFeng-Webnovel uses document-COMET. Higher is better. The complete comparison is in the report.
| Model | OPUS-Books | Biomedical | MTNT | MuST-Cinema | GuoFeng |
|---|---|---|---|---|---|
| Index-Translate-35B-A3B (preview) | 0.8013 | 0.8681 | 0.8244 | 0.8962 | 0.8291 |
| Index-Translate-9B | 0.8010 | 0.8674 | 0.8246 | 0.8979 | 0.8345 |
| Index-Translate-2B | 0.7845 | 0.8635 | 0.8132 | 0.8930 | 0.8345 |
These answer-accuracy results (%) use matched evaluation subsets. Translation training can change general capabilities; these scores describe the translation checkpoints rather than the original Qwen3.5 models.
| Model | C-Eval | GPQA-Diamond | INCLUDE | MMMLU |
|---|---|---|---|---|
| Index-Translate-35B-A3B (preview) | 77.6 | 49.1 | 68.8 | 71.9 |
| Index-Translate-9B | 69.6 | 36.3 | 63.1 | 66.6 |
| Index-Translate-2B | 42.6 | 27.4 | 39.1 | 42.7 |
The 1,024-token output budget in the inference client below is a usage default, not the output budget used to produce every reported evaluation result. Detailed protocols and valid-judgment counts are in the technical report. The report describes instTrans and MEME; benchmark release plans are tracked in the GitHub TODO.
The following settings describe the released translate.py client. Its decoding defaults apply across Index-Translate sizes; the commands below explicitly select this card's model.
| Setting | Client default and override |
|---|---|
| Decoding | Greedy, temperature=0; override with --temperature |
| Output budget | max_tokens=1024; override with --max-tokens |
| Thinking | Disabled through chat_template_kwargs={"enable_thinking": False}; no thinking toggle in this CLI |
| Top-p / top-k / min-p | Not explicitly set by this client; inference-server defaults apply |
| Presence / frequency / repetition penalties | Not explicitly set by this client; inference-server defaults apply |
| Random seed | Not explicitly set by this client; inference-server behavior applies |
| Stop strings / stop-token IDs / ignore-EOS | Not explicitly set by this client; inference-server and model defaults apply |
| Model ID | IndexTeam/Index-Translate-9B; INDEX_MODEL changes the default, and --model overrides it |
| Source language | auto; override with --source / -s; auto omits the source-language name from the prompt |
| Target language | en; override with --target / -t |
| Hard constraints | --hard / -H (repeatable, e.g. format preservation, code/tag protection) |
| Soft constraints | --soft / -S (repeatable, e.g. style/tone adaptation, domain sense disambiguation) |
| Glossary | Terminology mappings with --glossary / -g (src:tgt or JSON path); formatted into canonical hard term constraints |
| Genre / Domain | Source text genre/domain with --genre / -d (default: 文本, e.g. 专栏文章 / 学术论文 / 小说 / 结构化数据) |
| Instruction | General instruction with --instruction / -i (formatted into instTrans constraints) |
| Raw prompt | ; directly send input text without template wrapping |
The client maps supported short language codes to Chinese language names in the prompt. Provide the full language name for a code outside its mapping.
For standard unconstrained translation:
请将以下{source-language}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}
For constrained translation, the model follows the canonical instTrans format:
请将以下{source-language}{genre}翻译成{target-language}，并且严格遵循所有约束要求。
【源文】
{source-text}
【约束要求】
1. 【硬性要求】...
2. 【注意】...
只输出译文，不要有任何额外说明。
With --source auto, {source-language} is omitted. Prompt length, source text, and generated output all count toward the served context. Parameters left unspecified above are not assigned new fixed values by this model card.
The shipped configuration sets max_position_embeddings=262144. The examples below use --max-model-len 32768, matching the GitHub 2B/9B Translate serving preset. Input plus output must fit within the served context, and available GPU memory constrains practical lengths. Increase the client output budget for longer translations.
The GitHub inference guide gives an approximate BF16 GPU-memory starting point of 24 GB for 9B, with additional memory needed for KV cache and longer contexts.
Use a CUDA environment and a vLLM build with Qwen3.5 support. Start the server in one terminal:
pip install -U vllm
vllm serve IndexTeam/Index-Translate-9B \
--served-model-name IndexTeam/Index-Translate-9B \
--host 127.0.0.1 --port 8000 --max-model-len 32768
For a short-text smoke test, --max-model-len 4096 reduces the requested context. Multi-GPU deployments can add appropriate tensor-parallel arguments. See the serving guide for runtime details and other OpenAI-compatible servers.
Install the client dependencies in another terminal:
pip install openai httpx
This example uses the training-aligned Chinese translation prompt and disables thinking explicitly:
import httpx
from openai import OpenAI
client = OpenAI(
base_url="http://127.0.0.1:8000/v1",
api_key="EMPTY",
http_client=httpx.Client(trust_env=False),
)
# 1. Standard translation
response = client.chat.completions.create(
model="IndexTeam/Index-Translate-9B",
messages=[{
"role": "user",
"content": "请将以下文本翻译为英语，直接输出翻译结果，不要进行任何解释。\n\n你好，世界！",
}],
temperature=0,
max_tokens=1024,
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
text = response.choices[0].message.content or ""
if "</think>" in text: text = text.split("</think>", 1)[1]
print(text.strip().removeprefix("<think>").strip())
# 2. instTrans constrained translation (hard glossary + soft style constraints)
prompt_with_constraints = """请将以下专栏文章翻译成英语，并且严格遵循所有约束要求。
【源文】
王平仲采用了更加昂贵的碳纤维材料。碳纤维的好处就是它抗裂缝，而且能显著提升混凝土的刚性。
【约束要求】
1. 【硬性要求】专名/术语对照: 碳纤维→carbon fiber、抗裂缝→crack resistance、刚性→stiffness
2. 【注意】保持专业严谨的技术工程文档风格
只输出译文，不要有任何额外说明。"""
response = client.chat.completions.create(
model="IndexTeam/Index-Translate-9B",
messages=[{"role": "user", "content": prompt_with_constraints}],
temperature=0,
max_tokens=1024,
extra_body={"chat_template_kwargs": {"enable_thinking": False}},
)
text = response.choices[0].message.content or ""
if "</think>" in text: text = text.split("</think>", 1)[1]
print(text.strip().removeprefix("<think>").strip())
The model natively supports end-to-end multimodal image translation across 150 languages (zero OCR dependency), directly projecting image features into latent semantic space:
import base64
from openai import OpenAI
# 1. Read local image and encode to base64
with open("screenshot.png", "rb") as f:
b64_img = base64.b64encode(f.read()).decode("ascii")
# 2. Call OpenAI-compatible endpoint (public API or local serving)
client = OpenAI(
base_url="https://index-translate.bilibili.com/v1",
api_key="none",
default_headers={"User-Agent": "Index-Translate-Client/1.0"}
)
response = client.chat.completions.create(
model="IndexTeam/Index-Translate-9B",
messages=[{
"role": "user",
"content": [
{"type": "text", "text": "请将图片中的英文文本翻译为中文，直接输出翻译结果，不要进行任何解释。"},
{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64_img}"}},
]
}],
temperature=0.0,
max_tokens=1024,
extra_body={"chat_template_kwargs": {"enable_thinking": False}}
)
print(response.choices[0].message.content)
You can also use the official zero-dependency script inference/llm/call_api.py:
python inference/llm/call_api.py --image test.png --target zh -m Index-Translate-9B
Or test it directly in the interactive Web Demo. For quantized GGUF weights and visual projector files (mmproj), see Index-Translate-9B-GGUF. Full tutorial: Multimodal Image Translation Guide.
After starting the server, use the public client with this model ID:
git clone https://github.com/bilibili/Index-Translate.git
cd Index-Translate
pip install -r inference/llm/requirements.txt
# Basic translation
python inference/llm/translate.py \
"你好，世界。今天天气不错。" --target en \
--model IndexTeam/Index-Translate-9B
# Hard constraint: Terminology glossary (-g / --glossary)
python inference/llm/translate.py \
"王平仲采用了更加昂贵的碳纤维材料。碳纤维的好处就是它抗裂缝。" --target en \
-g "碳纤维:carbon fiber, 抗裂缝:crack resistance" \
--model IndexTeam/Index-Translate-9B
# Hard constraint: Structured data / JSON format preservation (-H / --hard)
python inference/llm/translate.py \
'{"user_id": 1024, "message": "您的订单已支付完成。"}' --target en \
-H "保留源文中的 JSON 格式标记不变" \
--model IndexTeam/Index-Translate-9B
# Soft constraint: Style (-S / --soft) & Genre (-d / --genre)
python inference/llm/translate.py \
"今天下午的会议临时取消了，改天我们再碰一下商量。" --target en \
-d "商务邮件" -S "调整为严谨、正式、礼貌的商务公文风格" \
--model IndexTeam/Index-Translate-9B
Use --source to specify the input language and --target for the output language. For languages without a mapped code in the client, provide the full language name. The client also accepts --base-url, --api-key, --max-tokens, and --temperature.
The following is a recorded Index-Translate-9B case from the official demo. The task asks for Korean translation while preserving the JSON structure, stars, and Chinese hashtag.
Source:
{"title": "⭐2月13日例行维护公告⭐", "content": "#热血航线大和登场#"}
Recorded translation:
{"title": "⭐2월 13일 정기 점검 공지⭐", "content": "#热血航线大和登场#"}
See the captured instruction cases for verified hard/soft constraint examples, translate cases, and prompt examples for more directions and instruction formats.
Scores vary across languages and tasks. Combined constraints and low-resource directions remain harder than core-language translation, and successful individual examples do not guarantee that every instruction will be followed. For explicit target syllable counts, use Index-Homura; for native full-document translation, use Index-NativeLong with its dedicated templates.
| Family | Models | Task |
|---|---|---|
| Index-Translate | 2B / 9B / 35B-A3B (preview) | Text translation and translation instructions |
| Index-Echo S2TT | 2B / 9B | Speech to translated subtitles |
| Index-Echo S2ST | 2B / 9B | Speech to speech with source-voice conditioning |
| Index-Homura | 2B / 9B | Translation toward a target syllable count |
| Index-NativeLong | 2B / 9B | Full-document translation |
Naming: Index-NativeLong retains the published model IDs IndexTeam/Index-Nailong-2B and IndexTeam/Index-Nailong-9B. The family repository includes prompt guides, browser-extension code, and a video-dubbing pipeline. This model's ModelScope page mirrors the model card.
@techreport{indextranslate2026,
author={Tianjiao Li and Mengran Yu and Chenyu Shi and Lusheng Zhang and
Qisi Chen and Yanshan Zhou and Ji Qi and Jingying Liu and
Yuang Feng and Ziang Cui and Tianxing Yan},
title={Index-Translate: A Multilingual Translation Model Family --- Text, Speech, Controlled Dubbing, and Long-Document Translation},
institution={Index LLM Team},
year={2026},
month={September},
url={https://arxiv.org/abs/2609.40181}
}
Apache-2.0. Questions and feedback are welcome through GitHub Issues.
| 0.8459 |
| 0.5812 |
| TranslateGemma-12B | 0.8732 | 0.8524 | 71.19 | 0.4515 | 0.3068 | 0.8023 | 0.2892 | 0.8347 | 0.4281 |
| North-Small-Translate (218B-A25B) | 0.8784 | 0.8578 | 68.37 | 0.5697 | 0.5294 | 0.7657 | 0.8635 | 0.8357 | 0.6836 |
| Qwen3.5-2B (base) | 0.6983 | 0.6933 | 32.11 | 0.0999 | 0.2431 | 0.6197 | 0.3836 | 0.7557 | 0.2062 |
| Qwen3.5-9B (base) | 0.8316 | 0.8073 | 60.31 | 0.2467 | 0.0609 | 0.7341 | 0.5980 | 0.8199 | 0.5728 |
| Qwen3.5-35B-A3B | 0.8570 | 0.8290 | 71.33 | 0.3690 | 0.5204 | 0.7589 | 0.7822 | 0.8267 | 0.6447 |
| DeepSeek-V4.1-Flash | 0.8762 | 0.8510 | 83.55 | 0.6068 | 0.6374 | 0.7817 | 0.9090 | 0.8432 | 0.7424 |
| GPT-5.6-Sol | 0.8650 | 0.8469 | 89.10 | 0.6902 | 0.7624 | 0.7946 | 0.9367 | 0.8311 | 0.7194 |
| Gemini 3.5 Flash Lite | 0.8750 | 0.8497 | 79.52 | 0.6068 | 0.6374 | 0.7764 | 0.8854 | 0.8131 | 0.7034 |
| 39.1% |
| 0.0069 |
| 0.0549 |
| 73.54% |
| Qwen3.5-9B (base) | 0.6774 | 0.4502 | 11.2% | 0.1318 | 0.3532 | 18.51% |
| Qwen3.5-35B-A3B | 0.7854 | 0.6257 | 3.5% | 0.2874 | 0.4458 | 11.60% |
| DeepSeek-V4.1-Flash | 0.8333 | 0.7297 | 1.3% | 0.4793 | 0.5854 | 5.73% |
| GPT-5.6-Sol | 0.7669 | 0.6918 | 10.4% | 0.5757 | 0.6866 | 7.73% |
| Gemini 3.5 Flash Lite | 0.8122 | 0.6995 | 3.3% | 0.3927 | 0.5584 | 12.18% |
--raw-prompt| Endpoint | http://127.0.0.1:8000/v1; OPENAI_BASE_URL changes the default, and --base-url overrides it |
| API key | EMPTY for local serving; OPENAI_API_KEY changes the default, and --api-key overrides it |
| Request format | One user message; no system message is added by the client |
| Response streaming | Not enabled by this client; it prints the complete response |
| Transport timeout and retries | No explicit values set by the script; installed OpenAI / HTTPX client behavior applies |
| Proxy handling | Environment proxies disabled for 127.0.0.1, localhost, and ::1; remote endpoints use the OpenAI client's usual behavior |
| Input / output | Positional text or stdin; input is stripped and empty input is rejected; output is stripped and any emitted thinking block is removed |