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[!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-indic-1B-ONNX(Current)- FP16 (Half Precision):
hari31416/indictrans2-indic-indic-1B-ONNX-fp16- INT8 (Dynamic Quantization):
hari31416/indictrans2-indic-indic-1B-ONNX-int8- Q4F16 (4-bit Block Quantization):
hari31416/indictrans2-indic-indic-1B-ONNX-q4f16
ai4bharat/indictrans2-indic-indic-1B
for in-browser and local edge inference.
| Format | Model Size | Exact Match (Token) | Exact Match (Text) | SacreBLEU (Raw) | Latency (Mean) | Speedup vs. FP32 |
|---|---|---|---|---|---|---|
| FP32 | 7.03 GB | 100.00% | 100.00% | 100.00 | 94.7 ms | 1.000x |
| FP16 | 3.52 GB | 99.82% | 99.82% | 100.00 | 108.3 ms | 0.874x |
| INT8 | 1.76 GB | 83.64% | 83.73% | 94.22 | 43.7 ms | 2.240x |
| Q4F16 | 900.0 MB | 73.18% | 73.18% | 89.33 | 94.2 ms | 1.087x |
encoder_model.onnx (and optional .onnx.data weights sidecar)decoder_model.onnx and decoder_with_past_model.onnx (share decoder_shared.onnx.data when present)translate.py — self-contained Python inference helper (see Usage below)tokenizer_src.json, tokenizer_tgt.json, tokenizer_meta.json)config.json, generation_config.json)1# translate.py is included in this repo alongside the ONNX bundle.
2# You can also find it (and read the full source) at:
3# https://github.com/Hari31416/indictrans2-onnx-export/blob/main/src/translate.py
4
5from translate import IndicTransONNX
6
7# Pass a HF repo ID for automatic download, or a local bundle directory path
8model = IndicTransONNX("hari31416/indictrans2-indic-indic-1B-ONNX")
9print(model.translate("चुनाव कौन जीतेगा?", src_lang="hin_Deva", tgt_lang="tam_Taml"))pip install onnxruntime tokenizers huggingface-hub