Merge pull request #2 from open-compass/eval

Merge Eval Branch to Main

README.md

@@ -9,22 +9,23 @@
 
 
 <div align="center">
- <img src="https://github.com/user-attachments/assets/d91b1b5d-c932-402c-b86d-2846620a68b0" width="800"/>
+ <img src="assets/pass-at-k-v-s-greedy-g-pass-at-k.png" width="800"/>
 </div>
 
 <!-- [🏰[Project Page](https://github.com/open-compass/GPassK/)]
 [📚[LeaderBoard](https://github.com/open-compass/GPassK/index.html)] -->
 
 ## 🚀 News
-- **[2024.12.18]** We release the **[ArXiv Paper](http://arxiv.org/abs/2412.13147)** of GPassK. 🎉🎉🎉
+- **[2025.1.6]** 🔥 **[LiveMathBench](https://huggingface.co/datasets/opencompass/LiveMathBench)** now can be accessed through hugginface, and you can now evaluate your LLMs on it using G-Pass@k in OpenCompass. We have addressed potential errors in LiveMathBench and inconsistencies in the sampling parameters. Please also refer to our updated version of the **[Paper](http://arxiv.org/abs/2412.13147)** for further details.
+- **[2024.12.18]** We release the **[ArXiv Paper](http://arxiv.org/abs/2412.13147)** of G-Pass@k. 🎉🎉🎉
 
 
 ## ☀️Introduction
 
 **G-Pass@k** is a novel evaluation metric that provides a continuous assessment of model performance across multiple sampling attempts, quantifying both the model’s peak performance potential and its stability. In addition, it comes with **LiveMathBench**, a dynamic benchmark comprising challenging, contemporary mathematical problems designed to minimize data leakage risks during evaluation. In order to track the latest performance and stability of LLMs, we will continue updating the benchmark with new comptition level mathmatical problems and provide the latest results of the models on the benchmark with G-Pass@k.
 
 
-## 🌲 Definition of GPassK
+## 🌲 Definition of G-Pass@k
 $$ \text{G-Pass@}k = \mathbb{E}_{\text{Questions}} \left[ \frac{{c \choose k}}{{n \choose k}} \right] $$ 
 
 where $n$ represents the total number of generations per question, and $c$ denotes the number
@@ -42,27 +43,95 @@ Intuitively, $\text{mG-Pass@}k$ provides an interpolated estimate of the area un
 *LiveMathBench-202412 version*
 
 <div align="center">
- <img src="https://github.com/user-attachments/assets/0e5d57c6-7fec-475e-acbe-cfa6aa2088cb" width="800"/>
+ <img src="assets/performance.png" width="800"/>
 </div>
 
 
-## 🖋Use GPassK in OpenCompass
+## 🖋Use G-Pass@k in OpenCompass
 [OpenCompass](https://github.com/open-compass/opencompass) is a toolkit for evaluating the performance of large language models (LLMs). To use GPassK in OpenCompass, you can follow the steps below:
-```python
-Coming Soon...
+
+### 1. Prepare Environment
+Follow these steps to ensure your environment is ready:
+
+```bash
+# Clone the main repository
+git clone https://github.com/open-compass/GPassK.git
+cd GPassK
+
+# Create and activate a conda environment with specific Python and PyTorch versions
+conda create -n livemathbench-eval python=3.10 pytorch torchvision torchaudio pytorch-cuda -c nvidia -c pytorch -y
+conda activate livemathbench-eval
+
+# Install additional required packages
+pip install loguru
+
+# Clone and install OpenCompass for extended functionality
+git clone https://github.com/open-compass/opencompass.git opencompass
+cd opencompass
+pip install -e .
+```
+
+
+### 2. Prepare Dataset
+LiveMathBench dataset can be obtained from HuggingFace. First, you should be granted to access the dataset from the following link: [huggingface](https://huggingface.co/datasets/opencompass/LiveMathBench).
+Then, refer to [security-tokens](https://huggingface.co/docs/hub/security-tokens) to set up your HF tokens.
+
+
+### 3. Deploy Judge Models
+We leverage Qwen2.5-72B-Instruct as the judge model for judging the correctness of generated answers. We recommend to deploy services using deployment tools such as [vllm](https://github.com/vllm-project/vllm) or [lmdeploy](https://github.com/InternLM/lmdeploy) for invocation by different evaluation tasks.
+
+Below is an example configuration for deploying the judge model using `lmdeploy`:
+```bash
+lmdeploy serve api_server Qwen/Qwen2.5-72B-Instruct --server-port 8000 \
+    --tp 4 \ # at least 4 A100 or equivalent GPUs are required
+    --cache-max-entry-count 0.9 \
+    --log-level INFO 
+```
+After setting up the judge model, define the URLs in the `eval_urls` within `opencompass_config_templates/*.py`. Adjust other parameters such as `k`， `temperatures`, `llm_infos`, and other params according to your needs.
+
+> ❗️Note that omitting `eval_urls` will default to an internal rule-based judge, which might only apply to datasets with numerical answers 
+
+### 4. Evaluation
+
+To begin the evaluation, first generate the necessary configuration files by running the following script:
+```bash
+python save_opencompass_configs.py --config_template_file {opencompass_config_templates/nono1.py|opencompass_config_templates/o1.py}
+```
+
+Upon execution, verify the generated configuration files located in `opencompass_configs/:
+
+```
+.
+├── deepseek-math-7b-rl_t0-3_p0-8_k50_rp1-0_rs42_l8192@LiveMathBench-v202412-k4_8_16-r3.py
+├── deepseek-math-7b-rl_t0-5_p0-8_k50_rp1-0_rs42_l8192@LiveMathBench-v202412-k4_8_16-r3.py
+├── deepseek-math-7b-rl_t0-7_p0-8_k50_rp1-0_rs42_l8192@LiveMathBench-v202412-k4_8_16-r3.py
+├── deepseek-math-7b-rl_t1-0_p0-8_k50_rp1-0_rs42_l8192@LiveMathBench-v202412-k4_8_16-r3.py
+```
+
+These files follow a naming convention that reflects the model settings and dataset used:
+```
+[MODEL_ABBR]_t[TEMPERATUE]_p[TOP_P]_k[TOP_K]_rp[REPETITION_PENALTY]_l[MAX_OUT_LEN]@[DATASET_ABBR]_k[LIST_OF_K]_r[REPLICATION].py
+```
+
+With the configurations prepared, initiate the evaluation process with the commands below:
+
+```bash
+cd GPassK
+conda activate livemathbench-eval
+python opencompass/run.py {path/to/config_file} \
+      -w ./opencompass_outputs/ \
+      --dump-eval-details \
 ```
+Refer to the OpenCompass documentation for additional arguments that may enhance your evaluation experience 
 
 
 # Citation and Tech Report
-If you use GPassK in your research, please cite the following paper:
+If you use G-Pass@k in your research, please cite the following paper:
 ```
-@misc{liu2024llmscapablestablereasoning,
-      title={Are Your LLMs Capable of Stable Reasoning?}, 
-      author={Junnan Liu and Hongwei Liu and Linchen Xiao and Ziyi Wang and Kuikun Liu and Songyang Gao and Wenwei Zhang and Songyang Zhang and Kai Chen},
-      year={2024},
-      eprint={2412.13147},
-      archivePrefix={arXiv},
-      primaryClass={cs.AI},
-      url={https://arxiv.org/abs/2412.13147}, 
+@article{liu2024your,
+  title={Are Your LLMs Capable of Stable Reasoning?},
+  author={Liu, Junnan and Liu, Hongwei and Xiao, Linchen and Wang, Ziyi and Liu, Kuikun and Gao, Songyang and Zhang, Wenwei and Zhang, Songyang and Chen, Kai},
+  journal={arXiv preprint arXiv:2412.13147},
+  year={2024}
 }
 ```

docs/LiveMathBench-A.csv

@@ -0,0 +1,20 @@
+Model,Greedy,[email protected],[email protected],[email protected],mG-Pass@16,link,opensourced,mathLM,o1-like
+Llama-3.1-8B-Instruct,24.0,18.2,11.3,4.55,10.4,https://github.com/facebookresearch/llama,TRUE,FALSE,FALSE
+Llama-3.1-70B-Instruct,29.8,30.0,22.2,12.5,20.8,https://github.com/facebookresearch/llama,TRUE,FALSE,FALSE
+Llama-3.3-70B-Instruct,40.3,36.2,28.9,19.1,27.5,https://github.com/facebookresearch/llama,TRUE,FALSE,FALSE
+Qwen2.5-7B-Instruct,37.0,36.5,27.2,16.0,25.8,https://github.com/QwenLM/Qwen,TRUE,FALSE,FALSE
+Qwen2.5-32B-Instruct,50.8,48.3,39.5,28.6,38.1,https://github.com/QwenLM/Qwen,TRUE,FALSE,FALSE
+Qwen2.5-72B-Instruct,51.7,47.3,39.6,29.0,37.8,https://github.com/QwenLM/Qwen,TRUE,FALSE,FALSE
+DeepSeek-V2.5-1210,38.7,38.9,27.9,17.3,26.7,https://github.com/deepseek-ai/DeepSeek-LLM,TRUE,FALSE,FALSE
+DeepSeek-V3.0-Chat,55.0,59.5,49.9,35.0,47.9,https://github.com/deepseek-ai/DeepSeek-V3,TRUE,FALSE,FALSE
+Mistral-Large-Instruct-2411-123B,41.6,39.4,37.1,32.9,36.4,https://example.com/mistral,TRUE,FALSE,FALSE
+Gemini-1.5-Pro-Latest,59.1,55.9,47.3,31.0,44.3,https://example.com/gemini,FALSE,FALSE,FALSE
+Claude-3.5-Sonnet,46.7,44.1,36.2,26.6,35.3,https://docs.anthropic.com/claude/docs/models-overview,FALSE,FALSE,FALSE
+GPT-4o-2024-11-20,44.8,41.9,32.9,22.2,31.6,https://openai.com/research/gpt-4,FALSE,FALSE,FALSE
+DeepSeek-Math-7B-RL,23.5,19.8,14.0,9.7,13.7,https://github.com/deepseek-ai/DeepSeek-LLM,TRUE,TRUE,FALSE
+NuminaMath-72B-CoT,40.8,34.0,27.1,14.2,25.0,https://example.com/numinamath,TRUE,TRUE,FALSE
+Qwen2.5-Math-7B-Instruct,44.1,44.1,38.3,28.1,36.6,https://github.com/QwenLM/Qwen,TRUE,TRUE,FALSE
+Qwen2.5-Math-72B-Instruct,57.6,52.7,45.4,27.9,42.3,https://github.com/QwenLM/Qwen,TRUE,TRUE,FALSE
+Skywork-o1-8B,45.4,39.3,31.9,21.7,30.4,https://example.com/skywork,TRUE,FALSE,TRUE
+QwQ-32B-Preview,72.7,74.9,65.8,40.1,61.2,https://example.com/qwq,TRUE,FALSE,TRUE
+OpenAI o1-mini,74.1,76.3,67.3,48.3,64.8,https://openai.com/research/o1,FALSE,FALSE,TRUE