---
license: mit
language:
  - as
  - bn
  - brx
  - doi
  - gu
  - hi
  - kn
  - ks
  - kok
  - mai
  - ml
  - mni
  - mr
  - ne
  - or
  - pa
  - sa
  - sat
  - sd
  - ta
  - te
  - ur
  - en
tags:
  - translation
  - onnx
  - indic
  - indictrans2
  - browser
  - fp32
pipeline_tag: translation
library_name: onnx
base_model: ai4bharat/indictrans2-indic-en-1B
---

# IndicTrans2 1B (indic→en) — ONNX bundle [FP32 (Full Precision)]

> [!TIP]
> This model is part of a suite of optimized/quantized ONNX versions of the base model.
> Other variants in this direction:
> - **FP32 (Full Precision / Base)**: [`hari31416/indictrans2-indic-en-1B-ONNX`](https://huggingface.co/hari31416/indictrans2-indic-en-1B-ONNX) *(Current)*
> - **FP16 (Half Precision)**: [`hari31416/indictrans2-indic-en-1B-ONNX-fp16`](https://huggingface.co/hari31416/indictrans2-indic-en-1B-ONNX-fp16)
> - **INT8 (Dynamic Quantization)**: [`hari31416/indictrans2-indic-en-1B-ONNX-int8`](https://huggingface.co/hari31416/indictrans2-indic-en-1B-ONNX-int8)
> - **Q4F16 (4-bit Block Quantization)**: [`hari31416/indictrans2-indic-en-1B-ONNX-q4f16`](https://huggingface.co/hari31416/indictrans2-indic-en-1B-ONNX-q4f16)

ONNX-exported and quantized version of [`ai4bharat/indictrans2-indic-en-1B`](https://huggingface.co/ai4bharat/indictrans2-indic-en-1B)
for in-browser and local edge inference.

- **Precision**: FP32 (Full Precision)
- **Description**: Baseline full-precision ONNX export.
- **Source Pipeline & Details**: For pipeline details, benchmarks, and usage instructions, see the [indictrans2-onnx-export GitHub repository](https://github.com/Hari31416/indictrans2-onnx-export).

Built for use with [Transformers.js](https://github.com/huggingface/transformers.js)
and [onnxruntime-web](https://onnxruntime.ai/docs/get-started/with-javascript.html)
in the browser, with fast BPE tokenizer.json files that don't require the
SentencePiece WASM runtime.

## Performance Visualizations

These charts show overall tradeoffs, language-level parity, and category breakdown.

![Overall Tradeoffs](./overall.png)
![Language-Level Parity](./languages.png)
![Category breakdown](./categories.png)

## Performance Tradeoffs & Size Comparison

Compared against the FP32 ONNX oracle on the golden evaluation fixtures.

| Format | Model Size | Exact Match (Token) | Exact Match (Text) | SacreBLEU (Raw) | Latency (Mean) | Speedup vs. FP32 |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| FP32 | 4.88 GB | 100.00% | 100.00% | 100.00 | 171.8 ms | 1.000x |
| FP16 | 2.44 GB | 99.91% | 99.91% | 99.98 | 180.5 ms | 0.952x |
| INT8 | 1.22 GB | 94.18% | 94.18% | 97.94 | 76.4 ms | 2.196x |
| Q4F16 | 625.0 MB | 87.55% | 87.55% | 95.17 | 85.5 ms | 1.962x |


## Files

- `encoder_model.onnx` (and optional `.onnx.data` weights sidecar)
- `decoder_model.onnx` (and optional `.onnx.data` weights sidecar)
- `decoder_with_past_model.onnx` (and optional `.onnx.data` weights sidecar)
- `translate.py` — self-contained Python inference helper (see Usage below)
- Fast tokenizer config files (`tokenizer_src.json`, `tokenizer_tgt.json`, `tokenizer_meta.json`)
- Model configuration configs (`config.json`, `generation_config.json`)

## Usage Example (Python, onnxruntime)

```python
# translate.py is included in this repo alongside the ONNX bundle.
# You can also find it (and read the full source) at:
#   https://github.com/Hari31416/indictrans2-onnx-export/blob/main/src/translate.py

from translate import IndicTransONNX

# Pass a HF repo ID for automatic download, or a local bundle directory path
model = IndicTransONNX("hari31416/indictrans2-indic-en-1B-ONNX")
print(model.translate("चुनाव कौन जीतेगा?", src_lang="hin_Deva", tgt_lang="eng_Latn"))
```

Required packages:

```bash
pip install onnxruntime tokenizers huggingface-hub
```

## License

MIT (preserved from upstream AI4Bharat).