indictrans2-en-indic-dist-200M-ONNX

IndicTrans2 200M (en→indic) — 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-en-indic-dist-200M-ONNX (Current)
• FP16 (Half Precision): hari31416/indictrans2-en-indic-dist-200M-ONNX-fp16
• INT8 (Dynamic Quantization): hari31416/indictrans2-en-indic-dist-200M-ONNX-int8
• Q4F16 (4-bit Block Quantization): hari31416/indictrans2-en-indic-dist-200M-ONNX-q4f16

ONNX-exported and quantized version of ai4bharat/indictrans2-en-indic-dist-200M 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.

Built for use with Transformers.js and onnxruntime-web 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.

Performance Tradeoffs & Size Comparison

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

Files

• 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)
• 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)

# 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-en-indic-dist-200M-ONNX")
print(model.translate("Who will win the election?", src_lang="eng_Latn", tgt_lang="hin_Deva"))

Required packages:

pip install onnxruntime tokenizers huggingface-hub

License

MIT (preserved from upstream AI4Bharat).