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Ismantic
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Model Details
Model Provider
Ismantic
Model Tree
Qwen/Qwen3-1.7B-Base
Input Modalities
Output Modalities
Supported Functionality
Unbabel/wmt22-comet-da)Full pipeline progression + ablations. This model is the + GRPO row.
| Stage | zh→en BLEU | zh→en COMET | en→zh BLEU | en→zh COMET |
|---|---|---|---|---|
| Qwen3-1.7B-Base (5-shot) | 21.83 | 0.7950 | 41.15 | 0.8490 |
| + SFT | 19.65 | 0.7863 | 40.74 | 0.8384 |
| + CPO (LoRA) | 19.16 | 0.8017 | 32.69 | 0.8507 |
| + GRPO (this model) | 20.31 | 0.8053 | 33.69 | 0.8540 |
| SFT → GRPO (skip CPO) | 22.85 | 0.8003 | 41.97 | 0.8540 |
| HY-MT1.5-1.8B (reference) | 17.84 | 0.8052 | 31.74 | 0.8669 |
zh→en COMET reaches parity with the 1.8B reference (0.8053 vs 0.8052); BLEU stays above the reference in both directions; en→zh COMET trails the larger reference by ~0.013.
The model uses the ChatML chat format with a fixed translation instruction; <|im_end|>
is the stop token. Greedy decoding is recommended.
python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torchmodel_id = "Ismantic/Interpreter-Qwen3-1.7B"tok = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained(model_id, 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:"prompt = f"<|im_start|>user\n{instr}<|im_end|>\n<|im_start|>assistant\n"ids = tok(prompt, return_tensors="pt").to(model.device)out = model.generate(**ids, max_new_tokens=256, do_sample=False,eos_token_id=tok.convert_tokens_to_ids("<|im_end|>"))return tok.decode(out[0][ids.input_ids.shape[1]:], 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"))print(translate("The quick brown fox jumps over the lazy dog.", "en2zh"))
vLLM works too (feed the same ChatML prompt, stop=["<|im_end|>"]).
For a quick interactive demo, grab translate.py from this repo and run
python translate.py (needs vllm) — type sentences, zh↔en auto-detected.
Qwen/Qwen3-1.7B-Basechosen replaced with COMET-better
HY-MT1.5-7B translations), then merged.wmt22-comet-da COMET reward plus a
4-gram repetition penalty, using WMT17–21 source prompts.Prompt template is fixed and identical across training and inference. BLEU tokenization:
zh for en→zh, 13a for zh→en. WMT23 is the primary (uncontaminated) test set; gains
were cross-checked on WMT23/24 + Flores-200 to rule out COMET reward-hacking.
Released under Apache-2.0, following the base model Qwen/Qwen3-1.7B-Base. Training data
derives from the ALMA / X-ALMA parallel & preference corpora and WMT news test sets;
HY-MT1.5-7B was used only to generate a subset of CPO preference targets. Please also
respect the licenses of those upstream models and datasets.