初始化项目，由ModelHub XC社区提供模型

Model: iamthewalrus67/kulyk-uk-en-grpo
Source: Original Platform

README.md

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+---
+language:
+  - uk
+  - en
+license_name: lfm1.0
+license_link: https://www.liquid.ai/lfm-license
+tags:
+  - translation
+  - grpo
+  - ukrainian
+  - lfm2
+base_model: Yehor/kulyk-uk-en
+pipeline_tag: text-generation
+---
+
+# kulyk-uk-en-grpo
+
+Ukrainian-to-English translation model based on [Yehor/kulyk-uk-en](https://huggingface.co/Yehor/kulyk-uk-en) (LFM2-350M), improved with GRPO using calibrated guardrail rewards on WikiMatrix data.
+
+## Results
+
+### FLoRes+ devtest (sentence-by-sentence, greedy, repetition_penalty=1.05)
+
+| Model | BLEU | chrF | CometKiwi |
+|-------|:----:|:----:|:---------:|
+| kulyk-uk-en (baseline) | 36.25 | 62.54 | 0.7401 |
+| **kulyk-uk-en-grpo** | **37.03** | **63.25** | **0.7436** |
+
+### WMT24 uk-en (news domain, out-of-distribution)
+
+| Model | BLEU | chrF | CometKiwi |
+|-------|:----:|:----:|:---------:|
+| kulyk-uk-en (baseline) | 27.90 | 54.49 | 0.6571 |
+| **kulyk-uk-en-grpo** | **28.25** | **54.54** | **0.6598** |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("iamthewalrus67/kulyk-uk-en-grpo", trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained("iamthewalrus67/kulyk-uk-en-grpo", trust_remote_code=True)
+
+prompt = "Translate the text to English:\nПогода сьогодні чудова."
+input_ids = tokenizer.apply_chat_template(
+    [{"role": "user", "content": prompt}],
+    add_generation_prompt=True, return_tensors="pt", tokenize=True
+).to(model.device)
+
+output = model.generate(input_ids, max_new_tokens=256, do_sample=False, repetition_penalty=1.05)
+print(tokenizer.decode(output[0][input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+## Training Details
+
+- **Method**: GRPO with calibrated guardrail rewards
+- **Data**: WikiMatrix uk-en (132K pairs)
+- **Rewards**: chrF (0.30) + BLEU (0.25) + CometKiwi (0.20) + 5x calibrated guardrails (0.05 each)
+- **Training**: Full fine-tune (no LoRA), single GPU, 300 steps
+- **Best checkpoint**: step 100
+
+See [reward-driven-translation](https://github.com/iamthewalrus67/reward-driven-translation) for full reproduction code.