Index-Translate-omni-9B-FP4

Built on the Index-Translate family, with multilingual text translation, terminology and formatting constraints, image translation, speech recognition, and bidirectional Chinese–English speech translation.

Original Model Reference

Source: Index-Translate-Omni model card. The tables below reproduce the upstream results for the original models. They are not new benchmark results for this quantized export; see the format-specific verification below for the checks actually performed here.

Text Translation

Model	FLORES COMET22 ↑	WMT24++ COMET22 ↑	WMT26 Judge ↑	MEME ↑
Index-Translate 2B	0.8655	0.8489	60.26	0.6443
Index-Translate-Omni 2B	0.8655	0.8489	60.26	0.6443
Index-Translate 9B	0.8789	0.8601	75.35	0.7387
Index-Translate-Omni 9B	0.8789	0.8601	75.35	0.7387
Index-Translate 35BA3B (preview)	0.8794	0.8586	76.76	0.7405
Index-Translate-Omni 35BA3B	0.8795	0.8593	78.01	0.7405

Instruction Following

Model	instTrans Quality / IFscore ↑	instTrans_minor Quality / IFscore ↑ / Off-target ↓	IFMTBench XCOMET-XXL / IFscore ↑
Index-Translate 2B	0.5391 / 0.7569	0.3050 / 0.6586 / 3.97%	0.7712 / 0.7584
Index-Translate-Omni 2B	0.5391 / 0.7569	0.3050 / 0.6586 / 3.97%	0.7712 / 0.7584
Index-Translate 9B	0.6771 / 0.8209	0.5222 / 0.7725 / 3.47%	0.7957 / 0.8760
Index-Translate-Omni 9B	0.6771 / 0.8209	0.5222 / 0.7725 / 3.47%	0.7957 / 0.8760
Index-Translate 35BA3B (preview)	0.6901 / 0.8336	0.5151 / 0.7715 / 4.05%	0.7926 / 0.8991
Index-Translate-Omni 35BA3B	0.6867 / 0.8371	0.5163 / 0.7843 / 3.62%	0.7934 / 0.9070

Original Index-Translate figures come from the published benchmarks. The original 35BA3B reference is the published preview checkpoint.

Speech

CER is a percentage (lower is better). BLEU and chrF2 are higher-is-better. Translation cells show BLEU / chrF2 / target CER.

Model	Chinese ASR CER ↓	English ASR CER ↓	English → Chinese	Chinese → English
Index-Translate-Omni 2B	13.8113	34.2560	36.982 / 30.357 / 43.3452	30.747 / 51.152 / 48.0670
Index-Translate-Omni 9B	17.8917	14.4097	43.507 / 36.312 / 41.3767	34.656 / 55.376 / 46.3720
Index-Translate-Omni 35BA3B	18.6803	15.0850	42.081 / 35.836 / 49.2490	36.404 / 57.323 / 45.1546

35BA3B uses a separate frontend for Chinese ASR; English ASR and speech translation use the native profile. Evaluation sample counts differ across model sizes.

Image Translation

COMET22 × 100 (higher is better). Each Omni model is compared with its Qwen3.5 baseline on paired evaluated subsets.

Model	MMTIT ↑	FLORES-rendered ↑	Vistra ↑
Qwen3.5-2B	52.164	50.187	55.326
Index-Translate-Omni 2B	65.809	53.612	65.596
Qwen3.5-9B	73.814	67.804	68.963
Index-Translate-Omni 9B	77.604	68.438	71.815
Qwen3.5-35B-A3B	75.602	70.005	71.067
Index-Translate-Omni 35BA3B	77.011	69.891	71.482

35BA3B reuses the verified image scores for the same weights. Results cover the evaluated subsets; the original failure and UNKNOWN scoring rules are retained.

FP4 Export

Quantized omni translation model with language and vision weights, original BF16 audio modules, and a portable runtime.

• Language: NVFP4 weights and calibrated activation scales, in 16-element blocks, exported with NVIDIA ModelOpt 0.44.
• Vision: original FP32 tensors, byte-identical to the assembled source.
• Audio: original BF16 audio encoder and connector, with a portable runtime.
• lm_head, token embeddings, linear-attention modules and MoE routers are excluded from quantization. Ordinary MLP gate_proj layers are quantized.

Hardware and loading: Native NVFP4 inference requires a compatible Blackwell-generation backend. H100 is not Blackwell. The included JointModel uses fp4_reference.py and ModelOpt to unpack and dequantize the saved weights to BF16 for correctness testing; this needs BF16-sized memory and is not an accelerated FP4 serving path. Standard AutoModelForImageTextToText.from_pretrained should not be used directly on this ModelOpt export. Native Blackwell serving has not been validated here.

Loading

Download the entire repository, including audio/ and audio-tokenizer/:

pip install -r requirements-joint.txt
python - <<'PY'
from joint_runtime import JointModel

model = JointModel('.', device_map='auto')
print(model.text_generate([{'role': 'user', 'content': 'Translate into English: 人工智能正在改变内容创作。'}]))
print(model.audio_generate('<audio>\nTranscribe the speech and translate it into English.', ['/path/to/audio.wav']))
PY

JointModel also supports image_generate(messages) with Transformers image-text messages. The runtime extracts only the required helpers from the supplied bound audio code; it does not require the original training repository. Keep the visual weights in FP32 and the audio weights in BF16.

Verification

Saved-checkpoint reload and text generation passed. The portable runtime also passed a text call, an image-description call, and one real WAV speech-to-English generation call on an H100. Text perplexity is 28.5391 over 250 held-out tokens using the reference loading mode above. These are small smoke tests, not a comprehensive translation, vision or speech-quality benchmark.

The tensor audit checked 128 quantized linear weights, 333 byte-identical visual tensors, 526 byte-identical audio tensors, and 299 byte-identical preserved language tensors. Exact outputs and limitations are in validation.json; file hashes are in SHA256SUMS.txt.

This repository does not include GGUF files; GGUF is a separate language/vision export without the end-to-end audio pipeline.

Mirrors

• Hugging Face: https://huggingface.co/IndexTeam/Index-Translate-omni-9B-FP4
• ModelScope: https://modelscope.cn/models/IndexTeam/Index-Translate-omni-9B-FP4