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license: mit
language:
- sa
- en
tags:
- sanskrit
- paraphrase
- diffusion
- d3pm
- pytorch
pipeline_tag: text2text-generation
---
# Sanskrit D3PM Encoder-Decoder Model
Roman/IAST Sanskrit input to Devanagari output using a custom D3PM checkpoint.
This package is configured for the `d3pm_encoder_decoder` checkpoint stored in
`best_model.pt`.
Hugging Face model repo: `bhsinghgrid/devflow2`
## Files Included
- `best_model.pt` — trained checkpoint
- `model_settings.json` — packaged runtime metadata
- `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"])
```
## Runtime Settings
For local/API usage, the runtime first reads `model_settings.json`, then allows
optional environment variable overrides:
- `HF_MODEL_TYPE` = `d3pm_cross_attention` or `d3pm_encoder_decoder`
- `HF_INCLUDE_NEG` = `true` or `false`
- `HF_NUM_STEPS` = diffusion step count for the packaged checkpoint
Packaged settings for this repo:
```bash
export HF_MODEL_TYPE=d3pm_encoder_decoder
export HF_INCLUDE_NEG=false
export HF_NUM_STEPS=4
```
## Use This Model In A Hugging Face Space
In your Space settings, set:
- `HF_CHECKPOINT_REPO=bhsinghgrid/devflow2`
- `HF_CHECKPOINT_FILE=best_model.pt`
If your Space reads model metadata automatically, no extra model-type variables
are required. If it does not, also set:
```bash
HF_DEFAULT_MODEL_TYPE=d3pm_encoder_decoder
HF_DEFAULT_INCLUDE_NEG=false
HF_DEFAULT_NUM_STEPS=4
```
## Transformer-Style Usage (Custom Runtime)
This checkpoint is a custom D3PM architecture (`.pt`), not a native `transformers`
`AutoModel` format. Use it 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": 4,
"clean_output": true
}
}
```
## Push This Folder To Model Hub
```bash
cd hf_model_repo_encoder_decoder
git add .
git commit -m "Add encoder-decoder T4 model package"
git push -u hf main
```
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