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+---
+license: apache-2.0
+datasets:
+- pjura/mahjong_board_states
+language:
+- zh
+base_model:
+- unsloth/Qwen3-4B-Instruct-2507
+tags:
+- riichi-mahjong
+- game-ai
+- qwen
+- qwen3
+- mahjong
+- discard-recommendation
+- gguf
+pipeline_tag: text-generation
+---
+
+# Qwen3-4B-Instruct-2507-mahjong-alpha
+
+`Qwen3-4B-Instruct-2507-mahjong-alpha` is a Riichi Mahjong domain model fine-tuned from `unsloth/Qwen3-4B-Instruct-2507` with QLoRA.
+
+It is designed for 4-player Riichi Mahjong discard recommendation: given round information, hand tiles, calls, visible tiles, tile-efficiency, and defense signals, the model outputs the single best discard tile for the current state.
+
+The current version is mainly intended for tool integration. The output is a single tile text only, without explanation.
+
+## Model Features
+
+- **Task**: 4-player Riichi Mahjong discard recommendation
+- **Base Model**: `unsloth/Qwen3-4B-Instruct-2507`
+- **Fine-tuning**: `QLoRA`
+- **Training Framework**: `Unsloth`
+- **Release Format**: `GGUF (F16)`
+- **Inference**: `llama.cpp`
+- **Maintainer**: `TTDXQ`
+
+## Scope
+
+This model targets 4-player Riichi Mahjong without red dora. The current version focuses only on discard recommendation. It does not provide full-game planning, yaku/score analysis, or detailed offense-defense explanations.
+
+## Limitations
+
+- Discard recommendation only
+- No full-game planning
+- No yaku, point calculation, or detailed strategic explanation
+- Not guaranteed for competitive or real-match performance
+- For research and learning purposes only
+
+## Prohibited Uses
+
+This model must not be used for:
+
+- cheating
+- game automation or plug-ins
+- account boosting or ghost-playing
+- real-money gambling assistance
+
+## Input and Output
+
+### Input Format
+
+The model input is a structured natural-language game-state description. Example:
+
+```text
+[情景分析]
+- 牌局: 东一局，你是庄家 (第1巡，牌墙余69张)。
+- 状态: 当前排名 1/4 (与一位差 0)。
+- 宝牌: 5万
+- 各玩家分数: 你有 25分, 下家: 25分, 对家: 25分, 上家: 25分。
+- 你的手牌: 1万 5万 7万 3筒 5筒 6筒 8筒 8筒 3索 5索 8索 南 白 发
+- 牌效: 5 向听，进张 82 张。
+- 防御：
+  最安全牌放铳率：11.3%
+  平均放铳率：18.5%
+  最危险牌放铳率：25.9%
+场上已见牌信息
+各玩家副露信息:本家副露：无, 下家副露：无, 对家副露：无, 上家副露：无
+各玩家牌河信息:本家：无, 下家：无, 对家：无, 上家：无
+
+[任务]
+根据当前情景，选择一张最应该打出的手牌。
+```
+
+### Output Format
+
+The output is strictly a single tile text without any prefix like "discard" and without explanation. Example:
+
+```text
+白
+```
+
+## How to Use
+
+### Inference with llama.cpp
+
+```bash
+llama-server -m Qwen3-4B-Instruct-2507-mahjong-alpha.gguf -c 2048
+```
+
+### Python Inference Example
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha"
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha"
+)
+
+# Prepare input
+input_text = "[情景分析]\n- 牌局: 东一局，你是庄家 (第1巡，牌墙余69张)。\n..."
+
+# Inference
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=10)
+result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(result)  # Output: 白
+```
+
+## Dataset
+
+The training data uses the 2018 subset of `pjura/mahjong_board_states`. This dataset originates from Tenhou.net gameplay records, with each record containing 511 data points covering game basics, dora indicators, player hand tiles, calls, discard piles, and discard decisions.
+
+### Data Processing
+
+The raw data was converted into human-readable natural language descriptions, with calculated turn numbers, actual dora, and simplified risk assessment. Sample distribution by turn:
+
+- Turns 1-3: 15%
+- Turns 4-6: 20%
+- Turns 7-12: 35%
+
+A total of `192000` samples were used, with no general instruction data or self-built data mixed in.
+
+- Train: `192000`
+- Validation: `2000`
+- Test: sampled as needed from 2019 data
+- Train / validation / test are fully non-overlapping
+
+### Dataset Citation
+
+```bibtex
+@dataset{mahjong_board_states,
+  title = {MahJong Board States Dataset},
+  author = {Patrick Jura},
+  year = {2024},
+  url = {https://huggingface.co/datasets/pjura/mahjong_board_states}
+}
+```
+
+## Training Details
+
+### Model Configuration
+- Base Model: `unsloth/Qwen3-4B-Instruct-2507`
+- Training Precision: `4bit`
+- Fine-tuning Method: `QLoRA`
+- Framework: `Unsloth`
+- Max Sequence Length: `2048`
+
+### LoRA Parameters
+- Rank: `128`
+- Alpha: `256`
+- Target Modules: All
+
+### Training Hyperparameters
+- Learning Rate: `1e-4`
+- LR Scheduler: `cosine`
+- Batch Size: `64`
+- Per-device Batch: `2`
+- Gradient Accumulation Steps: `32`
+- Training Steps: `3000`
+- Warmup Steps: `300`
+- Random Seed: `3407`
+- Load Best Checkpoint: Yes
+
+### Training Time
+- Total Duration: ~16.44 hours
+
+## Evaluation Results
+
+### Comparison with Dataset Actions
+
+Inference parameters: Temperature=0.1, Top_P=0.1
+
+**Metrics Explanation:**
+- Score: Max 500 points (1 point per correct sample, 0 for incorrect)
+- Full-match Rate: Samples where all 3 tests matched the dataset
+- Zero-score Rate: Samples where all 3 tests disagreed with the dataset
+
+#### Tile-Efficiency Test
+
+| Model | Method | Score | Full-match Rate | Zero-score Rate |
+|-------|--------|-------|----------------|-----------------|
+| Qwen3-4B | Prompt Engineering | 50.21 | 6.60% | 86.13% |
+| Qwen3-4B | Fine-tuned | 229.66 | 45.87% | 53.93% |
+| DeepSeek-V3.2 | Prompt Engineering | 181.66 | 21.40% | 46.33% |
+
+#### Defense Test
+
+| Model | Method | Score | Full-match Rate | Zero-score Rate |
+|-------|--------|-------|----------------|-----------------|
+| Qwen3-4B | Prompt Engineering | 53.55 | 6.17% | 84.43% |
+| Qwen3-4B | Fine-tuned | 239.89 | 47.93% | 52.00% |
+| DeepSeek-V3.2 | Prompt Engineering | 172.00 | 16.00% | 46.80% |
+
+#### Comprehensive Test
+
+| Model | Method | Score | Full-match Rate | Zero-score Rate |
+|-------|--------|-------|----------------|-----------------|
+| Qwen3-4B | Prompt Engineering | 53.44 | 0.60% | 84.40% |
+| Qwen3-4B | Fine-tuned | 233.33 | 46.53% | 53.20% |
+| DeepSeek-V3.2 | Prompt Engineering | 179.44 | 18.07% | 44.93% |
+
+### Comparison with Mortal
+
+Inference parameters: Temperature=0.6, Top_P=0.95
+
+#### Test 1: All Turn Data
+
+- Samples: 3000
+- Top-1 Accuracy: **50.73%**
+- Top-3 Accuracy: **83.37%**
+
+#### Test 2: Excluding Early Turns
+
+- Valid Samples: 3000
+- Top-1 Accuracy: **48.70%**
+- Top-3 Accuracy: **79.20%**
+
+> Note: Mortal is one of the strongest open-source Riichi Mahjong AIs currently available
+
+## Repository Links
+
+- GitHub: https://github.com/ttdxq/Qwen3-4B-Instruct-2507-mahjong-alpha
+- Hugging Face: https://huggingface.co/TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
+
+## License
+
+This model follows the Apache License 2.0.
+
+The training data comes from `pjura/mahjong_board_states`, which is licensed under `CC BY 4.0`. Please preserve the required attribution and citation when using it.
+
+## Acknowledgements
+
+Thanks to the following open-source resources:
+
+- `unsloth/Qwen3-4B-Instruct-2507`
+- `pjura/mahjong_board_states`
+- `Mortal`