---
license: mit
language:
- sa
- en
tags:
- sanskrit
- paraphrase
- diffusion
- d3pm
- pytorch
pipeline_tag: text2text-generation
---

# Sanskrit D3PM Paraphrase Model

Roman/IAST Sanskrit input to Devanagari output using a D3PM cross-attention model.

## Files Included

- `best_model.pt` — trained checkpoint
- `config.py` — runtime config
- `inference.py` — model loading + generation loop
- `inference_api.py` — simple Python API (`predict`)
- `handler.py` — Hugging Face Endpoint handler
- `model/`, `diffusion/` — architecture modules
- `sanskrit_src_tokenizer.json`, `sanskrit_tgt_tokenizer.json` — tokenizers

## Quick Local Test

```python
from inference_api import predict
print(predict("dharmo rakṣati rakṣitaḥ")["output"])
```

## Transformer-Style Usage (Custom Runtime)

This checkpoint is a custom D3PM architecture (`.pt`), not a native `transformers` `AutoModel` format.
Use it in a transformer-like way via the provided runtime:

```python
import torch
from config import CONFIG
from inference import load_model, run_inference, _decode_clean
from model.tokenizer import SanskritSourceTokenizer, SanskritTargetTokenizer

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model, cfg = load_model("best_model.pt", CONFIG, device)

src_tok = SanskritSourceTokenizer(vocab_size=16000, max_len=cfg["model"]["max_seq_len"])
tgt_tok = SanskritTargetTokenizer(vocab_size=16000, max_len=cfg["model"]["max_seq_len"])

text = "dharmo rakṣati rakṣitaḥ"
ids = torch.tensor([src_tok.encode(text)], dtype=torch.long, device=device)
out = run_inference(model, ids, cfg)
print(_decode_clean(tgt_tok, out[0].tolist()))
```

If you need full `transformers` compatibility (`AutoModel.from_pretrained`), export weights to a Hugging Face Transformers model format first.

## Endpoint Payload

```json
{
  "inputs": "yadā mano nivarteta viṣayebhyaḥ svabhāvataḥ",
  "parameters": {
    "temperature": 0.7,
    "top_k": 40,
    "repetition_penalty": 1.2,
    "diversity_penalty": 0.0,
    "num_steps": 64,
    "clean_output": true
  }
}
```

## Push This Folder To Model Hub

```bash
huggingface-cli login
huggingface-cli repo create <your-username>/sanskrit-d3pm --type model
cd hf_model_repo
git init
git lfs install
git remote add origin https://huggingface.co/<your-username>/sanskrit-d3pm
git add .
git commit -m "Initial model release"
git push -u origin main
```