---
license: apache-2.0
language:
- zh
- en
pipeline_tag: translation
tags:
- translation
- zh-en
- en-zh
- piece-tokenizer
- retok
- sft
- cpo
- grpo
---

# Interpreter-Qwen3-1.7B-ReTok

A 1.7B Chinese↔English translation model — the **PieceTokenizer (ReTok) A/B sibling** of
[`Ismantic/Interpreter-Qwen3-1.7B`](https://huggingface.co/Ismantic/Interpreter-Qwen3-1.7B).
Same SFT→CPO→GRPO pipeline, same data, same losses and rewards; the only differences are
the **tokenizer** (a custom SentencePiece-style *piece* tokenizer instead of Qwen3's HF BPE)
and the base checkpoint (a retokenized "phase-2 v18" piece base).

> ⚠️ **This is not a plug-and-play HF model.** The weights are standard, but the tokenizer is
> a compiled C++ `piece_tokenizer` extension + 5 sidecar files — `AutoTokenizer.from_pretrained`
> does **not** work. You must load it through the `PieceTokenizerWrapper` from the
> [Interpreter repo](https://github.com/Ismantic/Interpreter) (`ReTok/lib/`). See **Usage** below.

## Results (WMT23, COMET = `Unbabel/wmt22-comet-da`)

| Stage | zh→en BLEU / COMET | en→zh BLEU / COMET |
|---|---|---|
| base (5-shot) | 17.44 / 0.7582 | 38.10 / 0.8255 |
| SFT | 19.34 / 0.7762 | 40.09 / 0.8392 |
| CPO | 18.11 / 0.7941 | 31.38 / 0.8480 |
| **GRPO (this model)** | 18.43 / **0.7967** | 31.79 / **0.8511** |

COMET climbs monotonically from base → GRPO (+0.039 zh→en, +0.026 en→zh). Compared to the
HF-BPE sibling `Interpreter-Qwen3-1.7B`, the piece tokenizer costs ≈ −0.016 COMET at SFT but
the gap shrinks to ≈ −0.003 by GRPO — i.e. **CPO/GRPO recover most of the tokenizer-swap tax.**

## Files

Standard weights (`config.json`, `generation_config.json`, `model.safetensors`) plus the 5
self-contained piece-tokenizer artifacts: `piece.model`, `dict.txt`, `token_mapping.json`,
`special_tokens_map.json`, `tokenizer_config.json`.

## Usage

Requires the compiled `piece_tokenizer` extension and `PieceTokenizerWrapper` from the
Interpreter repo. Chat format is `<bos><user>{prompt}<assistant>{response}<eos>` (**not** ChatML),
`</s>` (id 2) is the stop token.

```python
# 1) get the loader:  git clone https://github.com/Ismantic/Interpreter
#    and build/install the piece_tokenizer C++ extension into your venv.
import sys, torch
sys.path.insert(0, "Interpreter/ReTok/lib")
from tokenizer_wrapper import PieceTokenizerWrapper
from transformers import AutoModelForCausalLM

model_dir = "path/to/Interpreter-Qwen3-1.7B-ReTok"   # this repo, downloaded
tok   = PieceTokenizerWrapper(model_dir)
model = AutoModelForCausalLM.from_pretrained(model_dir, torch_dtype=torch.bfloat16).cuda().eval()

def translate(text, direction="zh2en"):
    if direction == "zh2en":
        instr = f"Translate the following text from Chinese to English.\nChinese: {text}\nEnglish:"
    else:
        instr = f"Translate the following text from English to Chinese.\nEnglish: {text}\nChinese:"
    ids = tok.apply_chat_template([{"role": "user", "content": instr}],
                                  tokenize=True, add_generation_prompt=True)
    out = model.generate(torch.tensor([ids]).cuda(), max_new_tokens=256,
                         do_sample=False, eos_token_id=tok.eos_token_id)
    return tok.decode(list(out[0][len(ids):]), skip_special_tokens=True).strip()

print(translate("人工智能正在深刻改变我们的生活方式。", "zh2en"))
```

vLLM works with `skip_tokenizer_init=True` + `TokensPrompt(prompt_token_ids=...)` — see
`ReTok/eval/eval_vllm_piece.py` and `ReTok/RUN_BEST_MODEL.md` in the repo for a batch runner.

## Training

- Base: retokenized piece "phase-2 v18_tie" checkpoint (1.7B).
- SFT: full fine-tune on piece-format translation pairs (ALMA + X-ALMA, ~36.8K).
- CPO: LoRA preference training (DPO loss + NLL) on ~44K self-generated pairs, then merged.
- GRPO: full-parameter RL, reward = `wmt22-comet-da` COMET (1.0) + 4-gram repetition penalty (0.3),
  WMT17–21 source prompts. Reward/hyperparams are identical to the Qwen sibling (A/B contract).

## License & attribution

Apache-2.0. Derived from the Qwen3-1.7B model family and the ALMA / X-ALMA parallel & preference
corpora + WMT test sets; respect the upstream licenses. Piece tokenizer built for this project.