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-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
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-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
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-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
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-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
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1/model.py

@@ -0,0 +1,242 @@
+import json
+import torch
+import numpy as np
+import triton_python_backend_utils as pb_utils
+import uuid
+import transformers
+from typing import List, Dict, Any, Union, Tuple
+
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    GenerationConfig,
+    BitsAndBytesConfig,
+)
+from peft import PeftModel, PeftConfig
+
+class TritonPythonModel:
+    def initialize(self, args: Dict[str, str]):
+        """
+        모델 초기화: 라이브러리 버전 확인 및 모델/토크나이저 로드
+        """
+        self.logger = pb_utils.Logger
+        self.model_config = json.loads(args["model_config"])
+        self.model_name = args["model_name"]
+
+        # 1. 라이브러리 버전 로그 추가
+        # GGUF 로드를 위해서는 최소 4.40.0 이상을 권장합니다.
+        transformers_version = transformers.__version__
+        self.logger.log_info(f"================ {self.model_name} Setup ================")
+        self.logger.log_info(f"Transformers Version: {transformers_version}")
+        self.logger.log_info(f"Torch Version: {torch.__version__}")
+
+        # 설정 파라미터 로드
+        self.base_model_path = self._get_config_param("base_model_path")
+        self.gguf_filename = self._get_config_param("gguf_filename")
+        self.device = "cuda" if torch.cuda.is_available() else "cpu"
+
+        self.logger.log_info(f"Base Model Path: {self.base_model_path}")
+        self.logger.log_info(f"GGUF Filename: {self.gguf_filename}")
+        self.logger.log_info(f"Device: {self.device}")
+
+        # 2. 모델 및 토크나이저 로드 실행
+        self._load_model_and_tokenizer()
+        self.logger.log_info(f"Model initialized successfully.")
+
+    def _load_model_and_tokenizer(self):
+        """
+        config.pbtxt의 파라미터를 사용하여 GGUF 모델을 로드합니다.
+        Transformers 라이브러리가 GGUF를 읽어 fp16으로 역양자화합니다.
+        """
+        # 1. config.pbtxt에서 설정값 읽기
+        load_path = self.base_model_path  # /cheetah/input/model/groupuser/Qwen3-4B-Instruct-2507-mahjong-alpha
+        gguf_file = self._get_config_param("gguf_filename") # Qwen3-4B-Instruct-2507-mahjong-alpha.gguf
+
+        self.logger.log_info(f"Loading GGUF from: {load_path}/{gguf_file}")
+
+        try:
+            # 2. Tokenizer 로드 (GGUF 파일 내의 토크나이저 메타데이터 참조)
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                load_path, 
+                gguf_file=gguf_file,
+                trust_remote_code=True
+            )
+
+            # 3. Model 로드 (GGUF -> PyTorch fp16 변환)
+            # 주의: GGUF 로드 시 bnb_config(int4/8)와 중복 사용은 불가능할 수 있습니다.
+            self.model = AutoModelForCausalLM.from_pretrained(
+                load_path,
+                gguf_file=gguf_file,
+                torch_dtype=torch.float16,
+                device_map="auto",
+                local_files_only=True,
+                trust_remote_code=True
+            )
+            
+            self.model.eval()
+
+            # 패딩 토큰 설정
+            if self.tokenizer.pad_token is None:
+                self.tokenizer.pad_token = self.tokenizer.eos_token
+                self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
+
+            self.supports_chat_template = (
+                hasattr(self.tokenizer, "chat_template") and
+                self.tokenizer.chat_template is not None
+            )
+
+            self.logger.log_info("GGUF Model and Tokenizer loaded successfully via Transformers.")
+
+        except Exception as e:
+            self.logger.log_error(f"Failed to load GGUF model: {e}")
+            raise e
+
+    def _get_bnb_config(self) -> Union[BitsAndBytesConfig, None]:
+        if self.quantization == "int4":
+            return BitsAndBytesConfig(
+                load_in_4bit=True,
+                bnb_4bit_use_double_quant=True,
+                bnb_4bit_quant_type="nf4",
+                bnb_4bit_compute_dtype=torch.float16
+            )
+        elif self.quantization == "int8":
+            return BitsAndBytesConfig(
+                load_in_8bit=True,
+                llm_int8_threshold=6.0,
+                llm_int8_has_fp16_weight=True
+            )
+        return None
+
+    def execute(self, requests):
+        """Triton Inference Request 처리 메인 루프"""
+        responses = []
+
+        for request in requests:
+            # [ID 생성 로직] - 로그 추적용으로 유지 (Response에는 포함 X)
+            request_id = request.request_id()
+            if not request_id:
+                request_id = str(uuid.uuid4())
+
+            try:
+                # 1. 입력 데이터 파싱
+                input_data, is_chat = self._parse_input(request)
+
+                # [LOGGING] Request ID 포함하여 로그 출력
+                log_input_str = json.dumps(input_data, ensure_ascii=False) if isinstance(input_data, (list, dict)) else str(input_data)
+                self.logger.log_info(f"\n[RID: {request_id}] >>> [{'CHAT' if is_chat else 'TEXT'}][Input]: {log_input_str}")
+
+                # 2. Generation Config 생성
+                gen_config = self._create_generation_config(request)
+
+                # 3. 토크나이징
+                inputs = self._tokenize(input_data, is_chat)
+
+                # 4. 모델 추론 (Generate)
+                output_text = self._generate(inputs, gen_config)
+
+                # [LOGGING] Request ID 포함하여 결과 출력
+                self.logger.log_info(f"\n[RID: {request_id}] <<< [Output]: {output_text}")
+
+                # 5. 응답 생성
+                responses.append(self._create_response(output_text, request_id))
+
+            except Exception as e:
+                self.logger.log_error(f"[RID: {request_id}] Error during execution: {e}")
+                err_tensor = pb_utils.Tensor("text_output", np.array([str(e).encode('utf-8')], dtype=np.bytes_))
+                responses.append(pb_utils.InferenceResponse(output_tensors=[err_tensor]))
+
+        return responses
+
+    def _parse_input(self, request) -> Tuple[Union[str, List[Dict]], bool]:
+        input_text = self._get_input_scalar(request, "text_input")
+        try:
+            conversation = json.loads(input_text)
+            if isinstance(conversation, list):
+                return conversation, True
+        except (json.JSONDecodeError, TypeError):
+            pass
+        return input_text, False
+
+    def _tokenize(self, input_data, is_chat: bool):
+        if self.supports_chat_template and is_chat:
+            return self.tokenizer.apply_chat_template(
+                input_data,
+                tokenize=True,
+                add_generation_prompt=True,
+                return_tensors="pt",
+                return_dict=True
+            ).to(self.device)
+        else:
+            if is_chat:
+                 input_data = str(input_data)
+            return self.tokenizer(input_data, return_tensors="pt").to(self.device)
+
+    def _generate(self, inputs, gen_config: GenerationConfig) -> str:
+        input_ids = inputs["input_ids"]
+        input_len = input_ids.shape[-1]
+
+        with torch.no_grad():
+            outputs = self.model.generate(
+                **inputs,
+                generation_config=gen_config,
+                pad_token_id=self.tokenizer.pad_token_id,
+                eos_token_id=self.tokenizer.eos_token_id
+            )
+
+        generated_tokens = outputs[0][input_len:]
+        decoded_output = self.tokenizer.decode(generated_tokens, skip_special_tokens=True)
+        return decoded_output.strip()
+
+    def _create_generation_config(self, request) -> GenerationConfig:
+        def get_param(name, default=None, cast_type=None):
+            val = self._get_input_scalar(request, name, default)
+            if val is not None and cast_type:
+                return cast_type(val)
+            return val
+
+        return GenerationConfig(
+            max_length=get_param("max_length", 1024, int),
+            max_new_tokens=get_param("max_new_tokens", 256, int),
+            temperature=get_param("temperature", 1.0, float),
+            do_sample=get_param("do_sample", False, bool),
+            top_k=get_param("top_k", 50, int),
+            top_p=get_param("top_p", 1.0, float),
+            repetition_penalty=get_param("repetition_penalty", 1.0, float),
+        )
+
+    def _create_response(self, output_text: str, request_id: str):
+        """생성된 텍스트를 Triton Response 객체로 변환"""
+        output_tensor = pb_utils.Tensor(
+            "text_output",
+            np.array([output_text.encode('utf-8')], dtype=np.bytes_)
+        )
+        return pb_utils.InferenceResponse(output_tensors=[output_tensor])
+
+    def _get_config_param(self, key: str, default: str = None) -> str:
+        params = self.model_config.get('parameters', {})
+        if key in params:
+            return params[key].get('string_value', default)
+        return default
+
+    def _get_input_scalar(self, request, name: str, default=None):
+        tensor = pb_utils.get_input_tensor_by_name(request, name)
+        if tensor is None:
+            return default
+        return self._np_decoder(tensor.as_numpy()[0])
+
+    def _np_decoder(self, obj):
+        if isinstance(obj, bytes):
+           return obj.decode('utf-8')
+        if np.issubdtype(obj, np.integer):
+           return int(obj)
+        if np.issubdtype(obj, np.floating):
+           return round(float(obj), 3)
+        if isinstance(obj, np.bool_):
+           return bool(obj)
+
+    def finalize(self):
+        self.logger.log_info(f"Finalizing model {self.model_name}")
+        self.model = None
+        self.tokenizer = None
+        torch.cuda.empty_cache()
+        

README.md

@@ -1,251 +0,0 @@
----
-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` 是一个基于 `unsloth/Qwen3-4B-Instruct-2507` 进行 QLoRA 微调的立直麻将垂直模型，面向四麻弃牌建议任务。
-
-模型可根据输入的场次信息、手牌、副露、牌河、牌效与防守信息，输出当前最应打出的一张牌。
-
-当前版本主要面向工具集成场景，推理输出为单张牌文本，不包含解释信息。
-
-## 模型特点
-
-- **任务**：四麻立直麻将弃牌建议
-- **基座模型**：`unsloth/Qwen3-4B-Instruct-2507`
-- **微调方式**：`QLoRA`
-- **训练框架**：`Unsloth`
-- **发布格式**：`GGUF (F16)`
-- **推理方式**：`llama.cpp`
-- **维护者**：`TTDXQ`
-
-## 适用范围
-
-本模型面向四麻场景，不含赤宝牌。当前版本专注于"弃牌建议"这一单一任务，不提供完整对局规划，也不提供役种、打点或详细攻防解释。
-
-## 使用限制
-
-- 仅支持弃牌建议
-- 不支持完整对局规划
-- 不支持役种、打点、进攻防守解释
-- 不保证竞赛或实战效果
-- 仅供研究与学习使用
-
-## 禁止用途
-
-禁止将本模型用于：
-
-- 作弊
-- 外挂
-- 代打
-- 真钱赌博辅助
-
-## 模型输入输出
-
-### 输入格式
-
-模型输入为结构化自然语言局面描述。示例：
-
-```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。
-```
-
-### 输出格式
-
-模型输出严格为"单张牌文本"，不带"打"字，不带解释。例如：
-
-```text
-白
-```
-
-## 使用方法
-
-### llama.cpp 推理
-
-```bash
-llama-server -m Qwen3-4B-Instruct-2507-mahjong-alpha.gguf -c 2048
-```
-
-### Python 推理示例
-
-```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"
-)
-
-# 准备输入
-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..."
-
-# 推理
-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)  # 输出: 白
-```
-
-## 数据集
-
-训练数据使用 `pjura/mahjong_board_states` 的 2018 年部分数据。该数据集来源于天风麻将的游玩记录，每条数据包含 511 个数据点，涵盖游戏基础信息、宝牌指示牌、视角玩家手牌、玩家副露、牌河信息、玩家舍牌、弃牌决策等。
-
-### 数据处理
-
-将原始数据转换为便于阅读的自然语言描述形式，并根据数据计算出巡目数、实际宝牌、简易放铳参考等信息。根据巡目调整样本比例：
-
-- 1~3 巡：15%
-- 4~6 巡：20%
-- 7~12 巡：35%
-
-最终使用 `192000` 条样本，未混入通用指令数据或自建数据。
-
-- 训练集：`192000`
-- 验证集：`2000`
-- 测试集：`2019 年数据按需抽取`
-- 训练 / 验证 / 测试：完全互不重叠
-
-### 数据集引用
-
-```bibtex
-@dataset{mahjong_board_states,
-  title = {MahJong Board States Dataset},
-  author = {Patrick Jura},
-  year = {2024},
-  url = {https://huggingface.co/datasets/pjura/mahjong_board_states}
-}
-```
-
-## 训练信息
-
-### 模型配置
-- 基础模型：`unsloth/Qwen3-4B-Instruct-2507`
-- 训练加载精度：`4bit`
-- 微调方式：`QLoRA`
-- 训练框架：`Unsloth`
-- Max sequence length：`2048`
-
-### LoRA 参数
-- Rank：`128`
-- Alpha：`256`
-- 目标模块：全部
-
-### 训练超参数
-- Learning rate：`1e-4`
-- LR scheduler：`cosine`
-- Batch size：`64`
-- 单卡批次：`2`
-- 梯度累积步数：`32`
-- Training steps：`3000`
-- Warmup steps：`300`
-- Random seed：`3407`
-- 加载最优检查点：是
-
-### 训练时间
-- 总时长：约 16.44 小时
-
-## 评测结果
-
-### 与数据库弃牌动作对比
-
-推理参数：Temperature=0.1, Top_P=0.1
-
-**评测指标说明**：
-- 得分：满分 500 分（每个样本正确得 1 分，错误得 0 分）
-- 样本全对率：3 次测试均与测试集结果一致的样本占全部样本的比例
-- 样本零分率：3 次测试均与测试集结果不符的样本占全部样本的比例
-
-#### 牌效测试
-
-| 模型 | 方法 | 得分 | 样本全对率 | 样本零分率 |
-|------|------|------|------------|------------|
-| Qwen3-4B | 提示词工程 | 50.21 | 6.60% | 86.13% |
-| Qwen3-4B | 微调 | 229.66 | 45.87% | 53.93% |
-| DeepSeek-V3.2 | 提示词工程 | 181.66 | 21.40% | 46.33% |
-
-#### 防守测试
-
-| 模型 | 方法 | 得分 | 样本全对率 | 样本零分率 |
-|------|------|------|------------|------------|
-| Qwen3-4B | 提示词工程 | 53.55 | 6.17% | 84.43% |
-| Qwen3-4B | 微调 | 239.89 | 47.93% | 52.00% |
-| DeepSeek-V3.2 | 提示词工程 | 172.00 | 16.00% | 46.80% |
-
-#### 综合测试
-
-| 模型 | 方法 | 得分 | 样本全对率 | 样本零分率 |
-|------|------|------|------------|------------|
-| Qwen3-4B | 提示词工程 | 53.44 | 0.60% | 84.40% |
-| Qwen3-4B | 微调 | 233.33 | 46.53% | 53.20% |
-| DeepSeek-V3.2 | 提示词工程 | 179.44 | 18.07% | 44.93% |
-
-### 与 Mortal 对比
-
-推理参数：Temperature=0.6, Top_P=0.95
-
-#### 测试1：全部巡目数据
-
-- 样本数：3000
-- Top-1 准确率：**50.73%**
-- Top-3 准确率：**83.37%**
-
-#### 测试2：去除早巡数据
-
-- 有效样本数：3000
-- Top-1 准确率：**48.70%**
-- Top-3 准确率：**79.20%**
-
-> 注：Mortal 是当前开源最强的立直麻将 AI 之一
-
-## 仓库链接
-
-- GitHub：https://github.com/ttdxq/Qwen3-4B-Instruct-2507-mahjong-alpha
-- Hugging Face：https://huggingface.co/TTDXQ/Qwen3-4B-Instruct-2507-mahjong-alpha
-
-## License
-
-本模型遵循 Apache License 2.0 许可证。
-
-训练数据来自 `pjura/mahjong_board_states`，其许可证为 `CC BY 4.0`，使用时请保留相应署名与引用。
-
-## Acknowledgements
-
-感谢以下开源资源：
-
-- `unsloth/Qwen3-4B-Instruct-2507`
-- `pjura/mahjong_board_states`
-- `Mortal`

README_en.md

@@ -1,257 +0,0 @@
----
-license: apache-2.0
-datasets:
-- pjura/mahjong_board_states
-language:
-- zh
-- en
-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
-
-[中文](./README.md)
-
-`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
-```
-
-## Usage
-
-### llama.cpp Inference
-
-```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]
-- 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)。
-..."""
-
-# 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 is licensed under 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`

config.pbtxt

@@ -0,0 +1,135 @@
+# Triton Backend for TransformerLLM.
+backend: "python"
+max_batch_size: 0
+
+# Triton should expect as input a single string
+# input of variable length named 'text_input'
+input [
+
+  {
+    name: "text_input"
+    data_type: TYPE_STRING
+    dims: [ 1 ]
+
+
+  },
+  {
+    name: "max_length"
+    data_type: TYPE_INT32
+    dims: [ 1 ]
+
+    optional: true
+
+
+  },
+  {
+    name: "max_new_tokens"
+    data_type: TYPE_INT32
+    dims: [ 1 ]
+
+    optional: true
+
+
+  },
+  {
+    name: "do_sample"
+    data_type: TYPE_BOOL
+    dims: [ 1 ]
+
+    optional: true
+
+
+  },
+  {
+    name: "top_k"
+    data_type: TYPE_INT32
+    dims: [ 1 ]
+
+    optional: true
+
+
+  },
+  {
+    name: "top_p"
+    data_type: TYPE_FP32
+    dims: [ 1 ]
+
+    optional: true
+
+
+  },
+  {
+    name: "temperature"
+    data_type: TYPE_FP32
+    dims: [ 1 ]
+
+    optional: true
+
+
+  },
+  {
+    name: "repetition_penalty"
+    data_type: TYPE_FP32
+    dims: [ 1 ]
+
+    optional: true
+
+
+  },
+  {
+    name: "stream"
+    data_type: TYPE_BOOL
+    dims: [ 1 ]
+
+    optional: true
+
+
+  }
+
+]
+
+
+# Triton should expect to respond with a single string
+# output of variable length named 'text_output'
+output [
+
+  {
+    name: "text_output"
+    data_type: TYPE_STRING
+    dims: [ 1 ]
+
+  }
+
+]
+
+parameters: [
+  {
+    key: "base_model_path",
+    value: {string_value: "/cheetah/input/model/groupuser/Qwen3-4B-Instruct-2507-mahjong-alpha"}
+  },
+  {
+    key: "gguf_filename",
+    value: {string_value: "Qwen3-4B-Instruct-2507-mahjong-alpha.gguf"}
+  },
+  {
+    key: "is_adapter_model",
+    value: {string_value: "false"}
+  },
+  {
+    key: "adapter_model_path",
+    value: {string_value: ""}
+  },
+  
+  {
+    key: "quantization",
+    value: {string_value: "none"}
+  }
+]
+
+instance_group [
+  {
+    kind: KIND_AUTO
+    count: 1
+  }
+]
+