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README.md

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+---
+license: apache-2.0
+base_model: PrimeIntellect/Qwen3-0.6B
+tags:
+  - text-generation
+  - chinese
+  - sft
+  - qwen3
+datasets:
+  - ivanleomk/reverse-chinese-poems
+language:
+  - zh
+pipeline_tag: text-generation
+---
+
+# Reverse Chinese Text (SFT)
+
+This model is a fine-tuned version of [PrimeIntellect/Qwen3-0.6B](https://huggingface.co/PrimeIntellect/Qwen3-0.6B) trained on the task of reversing Chinese text character-by-character.
+
+## Training
+
+- **Base Model:** PrimeIntellect/Qwen3-0.6B
+- **Method:** Supervised Fine-Tuning (SFT)
+- **Dataset:** [ivanleomk/reverse-chinese-poems](https://huggingface.co/datasets/ivanleomk/reverse-chinese-poems)
+- **Training Steps:** 200
+- **Learning Rate:** 2e-5
+- **Batch Size:** 16
+- **Framework:** [Prime-RL](https://github.com/PrimeIntellect-ai/prime-rl)
+
+## Benchmark Results
+
+Evaluated on 1,000 samples from the test set:
+
+| Model | Character Accuracy | Exact Match Rate |
+|-------|-------------------|------------------|
+| PrimeIntellect/Qwen3-0.6B (base) | 0.10% | 0.00% |
+| **ivanleomk/reverse-chinese-text (SFT)** | **63.55%** | **9.60%** |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("ivanleomk/reverse-chinese-text")
+tokenizer = AutoTokenizer.from_pretrained("ivanleomk/reverse-chinese-text")
+
+messages = [
+    {"role": "system", "content": "You are a text reversal assistant. Given Chinese text, reverse it character by character."},
+    {"role": "user", "content": "请反转以下文字：床前明月光"}
+]
+
+input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
+output = model.generate(input_ids, max_new_tokens=100)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+# Expected: 光月明前床
+```
+
+## License
+
+Apache 2.0