初始化项目，由ModelHub XC社区提供模型

Model: Ismantic/Interpreter-Qwen3-1.7B
Source: Original Platform

README.md

@@ -0,0 +1,98 @@
+---
+license: apache-2.0
+base_model: Qwen/Qwen3-1.7B-Base
+language:
+- zh
+- en
+pipeline_tag: translation
+library_name: transformers
+tags:
+- translation
+- qwen3
+- zh-en
+- en-zh
+- sft
+- cpo
+- grpo
+datasets:
+- haoranxu/ALMA-Human-Parallel
+- haoranxu/X-ALMA-Parallel-Data
+---
+
+# Interpreter-Qwen3-1.7B
+
+A 1.7B Chinese↔English translation model, trained **SFT → CPO → GRPO**.
+
+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).
+
+| | this model |
+|---|---|
+| Tokenizer | Qwen3 native BBPE (151643) |
+| Chat format | ChatML |
+| Pipeline | SFT → CPO → GRPO |
+
+## Results (WMT23)
+
+| direction | BLEU / COMET |
+|---|---|
+| zh→en | 20.31 / 0.8053 |
+| en→zh | 33.69 / 0.8540 |
+
+### How to read these numbers
+
+WMT23 is the primary test set — **WMT22 is excluded**, roughly 17.5% of it leaked
+into the ALMA SFT data. COMET is `Unbabel/wmt22-comet-da`, which is *also* the
+GRPO reward, so gains were cross-checked on WMT23/24 + Flores-200 with BLEU and
+chrF to rule out reward hacking.
+
+vLLM greedy decoding is not bit-reproducible; BLEU varies by ~0.1 between runs.
+Differences at that scale are noise.
+
+## Usage
+
+Standard ChatML with a fixed translation instruction; `<|im_end|>` is the stop
+token. Greedy decoding recommended.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("Ismantic/Interpreter-Qwen3-1.7B")
+model = AutoModelForCausalLM.from_pretrained("Ismantic/Interpreter-Qwen3-1.7B", torch_dtype=torch.bfloat16).cuda().eval()
+
+instr = ("Translate the following text from Chinese to English.\n"
+         "Chinese: 人工智能正在深刻改变我们的生活方式。\nEnglish:")
+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|>"))
+print(tok.decode(out[0][ids.input_ids.shape[1]:], skip_special_tokens=True).strip())
+```
+
+vLLM works too — 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.
+
+## Training
+
+Three stages, all on a single RTX 4090:
+
+- **SFT** — full fine-tune on ~36.8K zh↔en pairs (ALMA + X-ALMA human parallel,
+  WMT22/23 leakage removed), loss on the response only.
+- **CPO** — LoRA preference training, `-log σ(β·(logπ_w − logπ_l)) + λ·NLL(y_w)`,
+  on ~44K **self-generated** preference pairs (5 candidates sampled from the SFT
+  model, best/worst picked by COMET). LoRA only — full-parameter CPO collapses
+  the model.
+- **GRPO** — full-parameter RL. Reward = reference-based `wmt22-comet-da` COMET
+  (1.0) + a 4-gram repetition penalty (0.3), over WMT17–21 source prompts.
+
+Training code, data provenance and the negative results (what was tried and
+rejected, with numbers) are in the
+[Interpreter repo](https://github.com/Ismantic/Interpreter).
+
+## License & attribution
+
+Apache-2.0, following the base model. Training data derives from the ALMA /
+X-ALMA parallel corpora and WMT news test sets. Please respect the licenses of
+those upstream models and datasets.