Update README.md

README.md

@@ -1,3 +1,82 @@
----
-license: apache-2.0
----
+---
+language:
+  - zh
+  - en
+library_name: pytorch
+tags:
+  - translation
+  - transformer
+  - tiny-llm
+  - zh-en
+pipeline_tag: translation
+---
+
+# Tiny-LLM (ZH→EN) Checkpoint
+
+Minimal Transformer encoder–decoder for Chinese → English translation. This repository hosts the inference assets (checkpoint and tokenizer) usable in Python or Gradio apps.
+
+## Files
+
+- `translate-step=290000.ckpt` — PyTorch state_dict checkpoint (Lightning-format state under `state_dict`)
+- `tokenizer.json` — Hugging Face Tokenizers (BPE) with special tokens `[UNK]`, `[PAD]`, `[SOS]`, `[EOS]`
+
+## Quick start
+
+Load files using `huggingface_hub` and run with your own model code:
+
+```python
+import torch
+from huggingface_hub import hf_hub_download
+from tokenizers import Tokenizer
+
+# Replace with your repo id if you fork
+REPO_ID = "caixiaoshun/tiny-llm-zh2en"
+
+ckpt_path = hf_hub_download(repo_id=REPO_ID, filename="translate-step=290000.ckpt")
+tokenizer_path = hf_hub_download(repo_id=REPO_ID, filename="tokenizer.json")
+
+# Example: integrate with a minimal Transformer implementation
+# from src.config import Config
+# from src.model import TranslateModel
+
+# config = Config()
+# config.tokenizer_file = tokenizer_path
+# model = TranslateModel(config)
+# state = torch.load(ckpt_path, map_location="cpu")["state_dict"]
+
+# # Strip potential Lightning/compile prefixes
+# prefix = "net._orig_mod."
+# state = { (k[len(prefix):] if k.startswith(prefix) else k): v for k, v in state.items() }
+# model.load_state_dict(state, strict=True)
+# model.eval()
+
+# tokenizer = Tokenizer.from_file(tokenizer_path)
+```
+
+If you deploy on Hugging Face Spaces or ModelScope, set environment variables to make your app fetch from this repo:
+
+```bash
+export HF_REPO_ID=caixiaoshun/tiny-llm-zh2en
+export CKPT_FILE=translate-step=290000.ckpt
+export TOKENIZER_FILE=tokenizer.json
+```
+
+## Notes
+
+- Trained on a Chinese→English parallel dataset (CSV layout with ZH at column 0 and EN at column 1). Ensure the tokenizer and model hyperparameters match your training run.
+- Decoding strategies supported in the reference app: greedy, nucleus (top-p), and beam search.
+
+## Intended use
+
+- Educational and demo purposes for small-scale translation tasks.
+- Not intended for production-grade translation quality without further training/finetuning and evaluation.
+
+## Limitations
+
+- Small model capacity; outputs may be inaccurate or inconsistent on complex inputs.
+- Tokenizer and checkpoint must match; mismatches lead to degraded results or load errors.
+
+## Acknowledgements
+
+- PyTorch for the deep learning framework
+- Hugging Face Tokenizers for fast BPE