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