| --- |
| 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 |
| ``` |
|
|
