[PZ COMPETITION] 添加ccabcca06的初赛报告、决赛报告和代码

openseek/competition/pz/ccabcca06/README.md

@@ -0,0 +1,181 @@
+# OpenSeek大模型挑战赛决赛提交代码使用指引
+**琶洲算法大赛“超越杯Openseek大模型挑战赛”:**
+https://deepvision.aicompetition-pz.com/#/homeDetail?id=1933438078467272705
+
+本仓库为决赛提交代码，集成了模型架构、数据集配置、训练与推理脚本，并提供了完整的复现指南及最终训练好的模型权重。
+## 训练部分
+包含VeRL的PPO训练配置文件：run_openseek_v1_ppo_step1.sh、run_openseek_v1_ppo_step2.sh
+### 训练环境配置
+
+**step1.拉取VeRL基础镜像：**  
+```
+docker pull verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.1-fa2.7.4
+```
+
+如dockerhub网络不稳定，可使用国内docker镜像源：
+```
+docker pull docker.m.daocloud.io/verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.1-fa2.8.0
+```
+
+**step2.从Modelscpoe拉取训练模型**
+```
+pip install modelscope # 安装modelscope库
+export MODELSCOPE_CACHE='/root/workspace' # 配置modelscope的下载路径
+
+modelscope download --model BAAI/OpenSeek-Small-v1-SFT # 下载起点模型(Actor模型)
+
+modelscope download --model deepseek-ai/DeepSeek-R1-0528-Qwen3-8B # 下载Critic模型
+
+```
+
+**step3.启动镜像：**
+```
+docker run -it --gpus=all --shm-size="10g" -v /root/workspace/models:/workspace/ckpt docker.m.daocloud.io/verlai/verl:base-verl0.5-cu126-cudnn9.8-torch2.7.1-fa2.8.0 # 启动镜像，并将模型下载路径挂载到容器
+```
+
+**step4.进入容器内安装:**
+```
+git clone https://github.com/volcengine/verl && cd verl
+pip3 install -e .[vllm]
+```
+**step5.准备数据集**
+
+下载gsm8k数据集并进行预处理
+
+```
+cd verl/examples/data_preprocess
+python gem8k.py
+```
+如无法访问huggingface，可以设置国内源：
+```
+os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'
+```
+下载并预处理gsm8k训练及测试集后，环境准备完毕，开始启动训练。
+
+### 第一阶段的PPO训练
+在8卡PCI-E的GPU服务器上训练，需要设置:
+```
+export NCCL_P2P_DISABLE=1
+```
+防止训练卡死。
+
+使用step1训练脚本：
+
+```
+bash run_openseek_v1_ppo_step1.sh
+```
+关键参数设置：
+```
+## run_openseek_v1_ppo_step1.sh
+
+# 配置训练、测试数据集路径
+data.train_files=/root/autodl-tmp/Openseek_RL/verl/data/gsm8k/train.parquet \
+data.val_files=/root/autodl-tmp/Openseek_RL/verl/data/gsm8k/test.parquet \
+
+# 配置actor、critic模型路径
+actor_rollout_ref.model.path=/workspace/model/BAAI/OpenSeek-Small-v1-SFT  # 竞赛起点模型
+ritic.model.path=/workspace/models/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B # deepseek r1 8b模型
+
+# 权重保存为hf格式
+save_contents=hf_model
+
+# 一阶段训练15个epoch
+trainer.total_epochs=15
+
+# 实验名称
+trainer.experiment_name='openseek_ppo_step1'
+```
+
+### 第二阶段的PPO训练
+重置critic避免模型过拟合
+
+使用step2训练脚本
+
+```
+bash run_openseek_v1_ppo_step2.sh
+```
+关键参数设置：
+```
+## run_openseek_v1_ppo_step1.sh
+
+# 配置训练、测试数据集路径(与阶段一一致)
+data.train_files=/root/autodl-tmp/Openseek_RL/verl/data/gsm8k/train.parquet \
+data.val_files=/root/autodl-tmp/Openseek_RL/verl/data/gsm8k/test.parquet \
+# 配置actor模型设置为一阶段训练保存的hf权重
+actor_rollout_ref.model.path=verl/checkpoints/verl_example/openseek_ppo_step1/actor/huggingface  
+# 将critic模型重置为R1-0528-Qwen3-8B初始模型
+critic.model.path=/workspace/models/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B 
+# 继续设置权重保存为hf格式
+save_contents=hf_model
+# 二阶段训练8个epoch
+trainer.total_epochs=8
+# 实验名称
+trainer.experiment_name='openseek_ppo_step2'
+```
+训练完毕后，在verl/checkpoints/verl_example/openseek_ppo_step2/actor/huggingface的路径下找到最终版本的hf模型。
+
+### 最终提交版本模型
+已完成2阶段训练的最终提交版本模型
+```
+modelscope download --model ccabcca06/Openseek-small-V1-PPO-ccabcca06 
+```
+## 推理评估部分
+评估源码基于 https://github.com/FlagAI-Open/OpenSeek/tree/main/evaluation/qwen_eval 进行修改，根目录：qwen_eval。
+
+### 环境配置
+**step1.安装latex2sympy**
+```
+cd qwen_eval/latex2sympy
+pip install -r requirements.txt
+pip install -e .
+```
+**step2.安装评测依赖**
+```
+cd ..
+pip install -r requirements.txt
+```
+**step3.安装math_verify**
+```
+pip install math_verify
+```
+
+### 配置prompt
+在utils.py中配置gsm8k和amc23专用prompt
+
+**GSM8K:**  
+在system prompt进行“You are an excellent mathematics professor”的身份设定后，在gsm8k数据集评分有大幅提升
+```
+    'prompt_gsm8k':(
+        "<|im_start|>system\nYou are an excellent mathematics professor<|im_end|>\n"
+        "<|im_start|>user\nPlease response it, and just put your final answer within \\boxed{{}}.\nQuestion:\n{input}<|im_end|>\n"
+        "<|im_start|>assistant\n",
+        "{output}",
+        "\n\n"
+    ),
+```
+**AMC23:**
+测试表明设置system_prompt会大幅降低模型在amc23数据集上的性能表现，故针对amc23数据集不进行角色设定
+```
+    'prompt_amc':(
+        "<|im_start|>system\n<|im_end|>\n"
+        "<|im_start|>user\nPlease response it, and just put your final answer within \\boxed{{}}.\nQuestion:\n{input}<|im_end|>\n"
+        "<|im_start|>assistant\n",
+        "{output}",
+        "\n\n"
+    ),
+```
+
+### 运行评估
+amc23数据集评估
+```
+bash run_eval_amc.sh ccabcca06/Openseek-small-V1-PPO-ccabcca06
+
+```
+其他数据集评估
+```
+bash run_eval_amc.sh ccabcca06/Openseek-small-V1-PPO-ccabcca06
+```
+聚合评估结果
+```
+python evaluate_final.py --eval_path ./outputs/eval_final
+```

openseek/competition/pz/ccabcca06/source_code/evaluate/qwen_eval/README.md

@@ -0,0 +1,53 @@
+## Requirements
+You can install the required packages with the following command:
+```bash
+cd latex2sympy
+pip install -e .
+cd ..
+pip install -r requirements.txt
+```
+
+
+## Usage
+### Generation & Evaluation
+
+To run the evaluation:
+
+1. **Configure the prompt type**: Add your custom prompt type in `utils.py` and specify it in `sh/run_evaluate.sh`
+
+2. **Set the model path**: Update the `MODEL_NAME_OR_PATH` variable in `sh/run_evaluate.sh` with your model's path
+
+3. **Run evaluation**: Execute the following command to generate predictions and evaluate results:
+   ```bash
+   bash sh/run_evaluate.sh
+   ```
+
+### Leaderboard
+```bash
+BASE_DIR=./eval_example/
+
+python evaluate_final.py --eval_path $BASE_DIR
+```
+BASE_DIR is the directory that contains different datasets folders. The structure of the directory is as follows:
+```bash
+BASE_DIR
+├── dataset1
+│   ├── example.jsonl
+├── dataset2
+│   ├── example.jsonl
+├── dataset3
+│   ├── example.jsonl
+```
+
+The final output file will be saved in the BASE_DIR folder. And the content of the file will be as follows:
+```
+{
+    "dataset_acc": {
+        "math500": 3.4,
+        "minerva_math": 3.6765,
+        "olympiadbench": 2.963,
+        "gsm8k": 3.6391
+    },
+    "final_acc": 3.41965
+}
+```