---
title: Index-Translate-2B-Joint-0bcfa9dd2254-FP8
canonical_url: "https://www.modelscope.cn/models/IndexTeam/Index-Translate-2B-Joint-0bcfa9dd2254-FP8"
md_url: "https://www.modelscope.cn/models/IndexTeam/Index-Translate-2B-Joint-0bcfa9dd2254-FP8.md"
repository: IndexTeam/Index-Translate-2B-Joint-0bcfa9dd2254-FP8
last_updated: 2026-11-05
license: apache-2.0
model_type:
  - qwen3_5
architectures:
  - Qwen3_5ForConditionalGeneration
parameters: 2.7B
tensor_type:
  - BF16
  - F8_E4M3
  - F32
library_name:
  - safetensors
language:
  - zh
  - en
downloads: 20
stars: 0
tags:
  - index-translate
  - multimodal
  - translation
  - image-to-text
  - automatic-speech-recognition
  - fp8
---

# Index-Translate-2B-Joint-0bcfa9dd2254-FP8

> Index-Translate-2B-Joint-0bcfa9dd2254-FP8 - IndexTeam 在 ModelScope 开源的模型。Index-Translate-2B-Joint-0bcfa9dd2254-FP8

IndexTeam/Index-Translate-2B-Joint-0bcfa9dd2254-FP8 是 ModelScope 魔搭社区上的 2.7B 参数机器学习模型，采用 apache-2.0 许可。

- **Repository**: IndexTeam/Index-Translate-2B-Joint-0bcfa9dd2254-FP8
- **License**: apache-2.0
- **Parameters**: 2.7B
- **Tags**: index-translate, multimodal, translation, image-to-text, automatic-speech-recognition, fp8
- **Downloads**: 20
- **Stars**: 0
- **Last updated**: 2026-11-05

Source: https://www.modelscope.cn/models/IndexTeam/Index-Translate-2B-Joint-0bcfa9dd2254-FP8

---

# Index-Translate-2B-Joint-0bcfa9dd2254-FP8

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](https://huggingface.co/IndexTeam/Index-Translate-omni-9B). 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](https://huggingface.co/IndexTeam/Index-Translate-9B#evaluation). 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.


## FP8 Export

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

- **Language:** FP8 weights with per-channel scales and dynamic per-token FP8 activations (`FP8_DYNAMIC`).
- **Vision:** original FP32 tensors, byte-identical to the assembled source.
- **Audio:** original BF16 audio encoder and connector, with a portable runtime.
- `lm_head` and token embeddings are not quantized. Linear-attention projections remain in the quantization recipe.

The included `JointModel` explicitly requests `dequantize=True` for compressed-tensors. This compatibility path uses BF16 compute after loading; it is not a claim of FP8 kernel acceleration.


## Loading

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

```bash
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 `41.1023` 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 186 quantized linear weights, 297 byte-identical visual tensors, 529 byte-identical audio tensors, and 135 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-2B-Joint-0bcfa9dd2254-FP8
- ModelScope: https://modelscope.cn/models/IndexTeam/Index-Translate-2B-Joint-0bcfa9dd2254-FP8