Index-Translate-35B-A3B-preview

Index-Translate-35B-A3B-preview is the 35B-total / 3B-active mixture-of-experts 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.

Preview: all 35B-A3B results below refer to the preview model evaluated in the technical report.

Capabilities

• General multilingual translation: translate sentences, articles, subtitles, and other text across the supported language inventory.
• Instruction following & constrained translation (硬约束 / 软约束):
  • Hard constraints (硬约束): strict glossary enforcement (术语强制对齐), structured data preservation (JSON, CSV, markdown formatting), code blocks, and variable placeholders (e.g. {variable}, [123456]).
  • Soft constraints (软约束): stylistic tone adaptation (formal, casual, social media / meme style), domain & context disambiguation (e.g. distinguishing industrial vs. botanical senses of terms).
• Social and cultural translation: interpret community aliases, playful spelling, memes, and nonliteral expressions with attention to their intended meaning.
• Has the highest FLORES COMET-22 (0.8794) and instTrans IFscore (0.8336) among all comparison systems in the report.
• Scores 0.6901 instTrans quality, 76.76 WMT26 Judge, and 0.7405 MEME.
• Among the three Index-Translate models, has the highest low-resource FLORES COMET-22 / XCOMET-XXL (0.8168 / 0.7164), with 2.4% off-target outputs. On low-resource instTrans, it scores 0.5151 quality / 0.7715 IFscore, with 4.05% off-target outputs.

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.

Training

The technical report describes a shared multilingual mid-training recipe, followed by task-specific post-training:

1. Multilingual mid-training: general text replay, monolingual text, ordinary parallel translations, and pivot-organized multilingual groups. The constant stage uses general / parallel / monolingual data at 1:1:1; decay uses general / core-pivot / full-pivot data at 1:4:2. The shared recipe totals 167.77B tokens.
2. Specialist SFT and RL: general translation, instruction following, and meme translation specialists receive task-specific supervision. General translation RL combines XCOMET-XXL with language-validity and adequacy judgments; instruction RL uses Rubric-as-Reward with hard checks and graded constraints. RIVAL provides adaptive judge supervision.
3. Expert integration and targeted distillation: parameter interpolation combines complementary specialists, then multi-teacher on-policy distillation (MOPD) addresses task types that remain weak after merging.

The report specifies 0.8 / 0.1 / 0.1 expert-interpolation weights for the evaluated 2B and 9B models. It does not assign these weights to the 35B-A3B preview.

Evaluation

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 preview with the other Index-Translate sizes, Hy-MT2-30B-A3B and Qwen3.5-35B-A3B as similarly sized MoE models, and larger translation models and API baselines.

Main translation benchmarks

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.

Low-resource translation and instruction following

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.

Domain translation

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.

General capabilities

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.

Evaluation protocol

• The main FLORES evaluation uses 126,000 inputs, 420 directions among 21 languages; WMT24++ uses 6,000 inputs from English into 20 languages.
• instTrans has 3,000 Chinese-to-20-language tasks across ten constraint types. GPT-5.6-Sol judges quality and instruction adherence separately. MEME contains 3,638 Chinese-to-English cases, covering 703 terms and 857 disambiguated senses.
• Text translation evaluation uses greedy decoding and a 16,384-token context window, except WMT26, which uses zero-shot Chinese instructions, a 3,584-token context, and a 1,536-token output limit. WMT26 reference-free GPT-5.6-Sol scores are averaged over 20 target languages.
• IFMTBench starts with 7,344 examples. Preprocessing removes 280 single-constraint examples with reference or language-label issues, leaving 7,064 inputs; 7,063 receive valid judgments. See the preprocessing note.

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.

Inference

Default settings

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.

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.

Serving this model requires a runtime supporting Qwen3.5 MoE and the complete checkpoint, including every weight shard listed in model.safetensors.index.json. The commands below target its public model ID.

Serve with vLLM

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-35B-A3B-preview \
  --served-model-name IndexTeam/Index-Translate-35B-A3B-preview \
  --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.

OpenAI-compatible Python client

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-35B-A3B-preview",
    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-35B-A3B-preview",
    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())

Repository client

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-35B-A3B-preview

# 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-35B-A3B-preview

# 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-35B-A3B-preview

# 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-35B-A3B-preview

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.

Examples and practical limits

The prompt guide illustrates terminology, structure, style, and context instructions. Recorded text examples in the demo, instruction cases, and inference cases are labeled by model; the 2B/9B examples are not 35B-A3B outputs.

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.

Model family

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.

Citation

@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}
}

License

Apache-2.0. Questions and feedback are welcome through GitHub Issues.