99 lines
3.4 KiB
Markdown
99 lines
3.4 KiB
Markdown
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-1.7B-Base
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language:
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- zh
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- en
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pipeline_tag: translation
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library_name: transformers
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tags:
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- translation
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- qwen3
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- zh-en
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- en-zh
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- sft
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- cpo
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- grpo
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datasets:
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- haoranxu/ALMA-Human-Parallel
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- haoranxu/X-ALMA-Parallel-Data
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---
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# Interpreter-Qwen3-1.7B
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A 1.7B Chinese↔English translation model, trained **SFT → CPO → GRPO**.
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This model is one half of an A/B pair. The two differ in **exactly two things** — the base checkpoint and the tokenizer — and share the same data, hyperparameters, losses and rewards. The other half is [`Ismantic/Interpreter-Qwen3-1.7B-ReTok`](https://huggingface.co/Ismantic/Interpreter-Qwen3-1.7B-ReTok).
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| | this model |
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| Tokenizer | Qwen3 native BBPE (151643) |
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| Chat format | ChatML |
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| Pipeline | SFT → CPO → GRPO |
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## Results (WMT23)
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| direction | BLEU / COMET |
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|---|---|
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| zh→en | 20.31 / 0.8053 |
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| en→zh | 33.69 / 0.8540 |
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### How to read these numbers
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WMT23 is the primary test set — **WMT22 is excluded**, roughly 17.5% of it leaked
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into the ALMA SFT data. COMET is `Unbabel/wmt22-comet-da`, which is *also* the
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GRPO reward, so gains were cross-checked on WMT23/24 + Flores-200 with BLEU and
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chrF to rule out reward hacking.
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vLLM greedy decoding is not bit-reproducible; BLEU varies by ~0.1 between runs.
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Differences at that scale are noise.
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## Usage
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Standard ChatML with a fixed translation instruction; `<|im_end|>` is the stop
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token. Greedy decoding recommended.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tok = AutoTokenizer.from_pretrained("Ismantic/Interpreter-Qwen3-1.7B")
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model = AutoModelForCausalLM.from_pretrained("Ismantic/Interpreter-Qwen3-1.7B", torch_dtype=torch.bfloat16).cuda().eval()
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instr = ("Translate the following text from Chinese to English.\n"
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"Chinese: 人工智能正在深刻改变我们的生活方式。\nEnglish:")
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prompt = f"<|im_start|>user\n{instr}<|im_end|>\n<|im_start|>assistant\n"
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ids = tok(prompt, return_tensors="pt").to(model.device)
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out = model.generate(**ids, max_new_tokens=256, do_sample=False,
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eos_token_id=tok.convert_tokens_to_ids("<|im_end|>"))
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print(tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True).strip())
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```
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vLLM works too — same ChatML prompt, `stop=["<|im_end|>"]`.
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For a quick interactive demo, grab `translate.py` from this repo and run
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`python translate.py` (needs `vllm`) — type sentences, zh↔en auto-detected.
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## Training
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Three stages, all on a single RTX 4090:
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- **SFT** — full fine-tune on ~36.8K zh↔en pairs (ALMA + X-ALMA human parallel,
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WMT22/23 leakage removed), loss on the response only.
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- **CPO** — LoRA preference training, `-log σ(β·(logπ_w − logπ_l)) + λ·NLL(y_w)`,
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on ~44K **self-generated** preference pairs (5 candidates sampled from the SFT
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model, best/worst picked by COMET). LoRA only — full-parameter CPO collapses
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the model.
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- **GRPO** — full-parameter RL. Reward = reference-based `wmt22-comet-da` COMET
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(1.0) + a 4-gram repetition penalty (0.3), over WMT17–21 source prompts.
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Training code, data provenance and the negative results (what was tried and
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rejected, with numbers) are in the
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[Interpreter repo](https://github.com/Ismantic/Interpreter).
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## License & attribution
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Apache-2.0, following the base model. Training data derives from the ALMA /
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X-ALMA parallel corpora and WMT news test sets. Please respect the licenses of
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those upstream models and datasets.
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