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<title>Memorization or Generation of Big Code Model Leaderboard</title> |
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<h1> |
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⭐ <span style="color: rgb(223, 194, 25);">Memorization</span> or |
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<span style="color: rgb(223, 194, 25);">Generation</span> |
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of Big |
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<span style="color: rgb(223, 194, 25);">Code</span> |
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Models |
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<span style="color: rgb(223, 194, 25);">Leaderboard</span> |
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</h1> |
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<div class="section_title__imgs"> |
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<a href="https://github.com/YihongDong/CDD-TED4LLMs" id="a_github" target="_blank"> |
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<img src="https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white"> |
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</a> |
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<a href="https://arxiv.org/abs/2402.15938" id="a_arxiv" target="_blank"> |
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<img src="https://img.shields.io/badge/PAPER-ACL'24-ad64d4.svg?style=for-the-badge"> |
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</a> |
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<div class="section_title__p"> |
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<p>Inspired from the <a href="https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard" target="_blank">🤗 Open LLM Leaderboard</a> and |
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<a href="https://huggingface.co/spaces/optimum/llm-perf-leaderboard" target="_blank">🤗 Open LLM-Perf Leaderboard 🏋️</a>, |
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we compare performance of base code generation models on |
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<a href="https://huggingface.co/datasets/openai_humaneval" target="_blank">HumanEval</a> and |
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<a href="https://huggingface.co/datasets/dz1/CodeScore-HumanEval-ET" target="_blank">HumanEval-ET</a> benchamrk. We also measure Memorization-Generalization Index and |
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provide information about the models. |
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We only compare open pre-trained code models, that people can start from as base models for |
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their trainings. |
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</p> |
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</section> |
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<section class="section_button"> |
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<button id="btn_evalTable">🔍 Evalution Table</button> |
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<button id="btn_plot">📊 Performance Plot</button> |
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<button id="btn_about">📝 About</button> |
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<button id="btn_submit">🚀 Submit results</button> |
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<section class="section_evalTable" id="sec_evalTable"> |
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<div class="section_evalTable__table"> |
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<table id="evalTable"> |
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<th rowspan="2">Model |
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<button class="button_sort" data-direction="desc" data-type="name"></button> |
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</th> |
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<th data-direction="desc" rowspan="2" data-type="MGI">MGI |
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<button class="button_sort" data-direction="desc" data-type="MGI"></button> |
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</th> |
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<th colspan="2">Pass@1(temp=0)</th> |
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<th colspan="2">Pass@1(temp=0.8)</th> |
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<tr> |
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<th>HumanEval |
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<button class="button_sort" data-direction="desc" data-type="temp0_HumanEval"></button> |
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</th> |
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<th>HumanEval-ET |
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<button class="button_sort" data-direction="desc" data-type="temp0_HumanEval_ET"></button> |
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</th> |
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<th>HumanEval |
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<button class="button_sort" data-direction="desc" data-type="temp0_8_HumanEval"></button> |
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</th> |
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<th>HumanEval-ET |
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<button class="button_sort" data-direction="desc" data-type="temp0_8_HumanEval_ET"></button> |
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</th> |
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</tr> |
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</thead> |
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</table> |
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</table> |
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<div class="section_evalTable__notes"> |
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<p><strong>Notes</strong> |
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<p> |
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<ul> |
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<li>MGI represents Memorization-Generalization Index, originally referred to as Avg. Peak.</li> |
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<li>For more details check the 📝 About section.</li> |
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</ul> |
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</div> |
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</section> |
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<section class="section_plot" id="sec_plot"> |
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<div style="display: flex;"> |
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<div class="section_plot__div" id="sec_plot__div1"> |
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<div class="section_plot__btnGroup" id="sec_plot__btnGroup1"> |
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<button id="btn_temp0_HumanEval"></button> |
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<span id="span_temp0_HumanEval">HumanEval</span> |
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<button id="btn_temp0_HumanEval_ET"></button> |
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<span id="span_temp0_HumanEval_ET">HumanEval-ET</span> |
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<div id="sec_plot__chart1" style="width:736.5px; height:600px;"></div> |
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<div class="section_plot__btnGroup" id="sec_plot__btnGroup2"> |
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<button id="btn_temp0_8_HumanEval"></button> |
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<span id="span_temp0_8_HumanEval">HumanEval</span> |
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<button id="btn_temp0_8_HumanEval_ET"></button> |
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<span id="span_temp0_8_HumanEval_ET">HumanEval-ET</span> |
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</div> |
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<div id="sec_plot__chart2" style="width:736.5px; height:600px;"></div> |
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</div> |
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<script src="chart.js"></script> |
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</section> |
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<section class="section_about" id="sec_about"> |
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<h2>Context</h2> |
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<div> |
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<p>The growing number of code models released by the community necessitates a comprehensive evaluation to |
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reliably benchmark their capabilities. |
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Similar to the 🤗 Open LLM Leaderboard, we selected two common benchmarks for evaluating Code LLMs on |
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multiple programming languages:</p> |
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<ul> |
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<li>HumanEval - benchmark for measuring functional correctness for synthesizing programs from |
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docstrings. It consists of 164 Python programming problems.</li> |
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<li>MultiPL-E - Translation of HumanEval to 18 programming languages.</li> |
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<li>Throughput Measurement - In addition to these benchmarks, we also measure model throughput on a |
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batch size of 1 and 50 to compare their inference speed.</li> |
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</ul> |
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<h3>Benchmark & Prompts</h3> |
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<ul> |
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<li>HumanEval-Python reports the pass@1 on HumanEval; the rest is from MultiPL-E benchmark.</li> |
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<li>For all languages, we use the original benchamrk prompts for all models except HumanEval-Python, |
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where we separate base from instruction models. |
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We use the original code completion prompts for HumanEval for all base models, but for Instruction |
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models, |
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we use the Instruction version of HumanEval in HumanEvalSynthesize delimited by the tokens/text |
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recommended by the authors of each model |
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(we also use a max generation length of 2048 instead of 512).</li> |
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</ul> |
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<p>Figure below shows the example of OctoCoder vs Base HumanEval prompt, you can find the other prompts |
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here.</p> |
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</div> |
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<div> |
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<p>- An exception to this is the Phind models. They seem to follow to base prompts better than the |
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instruction versions. |
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Therefore, following the authors' recommendation we use base HumanEval prompts without stripping them of |
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the last newline. |
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- Also note that for WizardCoder-Python-34B-V1.0 & WizardCoder-Python-13B-V1.0 (CodeLLaMa based), |
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we use the HumanEval-Python instruction prompt that the original authors used with their postprocessing |
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(instead of HumanEvalSynthesize), |
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code is available [here](https://github.com/bigcode-project/bigcode-evaluation-harness/pull/133).</p> |
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<h3>Evalution Parameters</h3> |
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<ul> |
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<li>All models were evaluated with the bigcode-evaluation-harness with top-p=0.95, temperature=0.2, |
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max_length_generation 512, and n_samples=50.</li> |
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</ul> |
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<h3>Throughput and Memory Usage</h3> |
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<ul> |
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<li>Throughputs and peak memory usage are measured using Optimum-Benchmark which powers Open LLM-Perf |
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Leaderboard. (0 throughput corresponds to OOM).</li> |
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</ul> |
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<h3>Scoring and Rankings</h3> |
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<ul> |
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<li>Average score is the average pass@1 over all languages. For Win Rate, we find model rank for each |
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language and compute num_models - (rank -1), then average this result over all languages.</li> |
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</ul> |
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<h3>Miscellaneous</h3> |
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<ul> |
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<li>#Languages column represents the number of programming languages included during the pretraining. |
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UNK means the number of languages is unknown.</li> |
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</ul> |
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</div> |
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</section> |
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<section class="section_submit" id="sec_submit"> |
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<h2>How to submit models/results to the leaderboard?</h2> |
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<div> |
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<p>We welcome the community to submit evaluation results of new models. These results will be added as |
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non-verified, the authors are however required to upload their generations in case other members want to |
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check.</p> |
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<h3>1 - Running Evaluation</h3> |
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<p>We wrote a detailed guide for running the evaluation on your model. You can find the it in |
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bigcode-evaluation-harness/leaderboard. This will generate a json file summarizing the results, in |
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addition to the raw generations and metric files.</p> |
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<h3>2- Submitting Results 🚀</h3> |
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<p>To submit your results create a Pull Request in the community tab to add them under the folder |
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community_results in this repository:</p> |
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<ul> |
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<li>Create a folder called ORG_MODELNAME_USERNAME for example bigcode_starcoder_loubnabnl</li> |
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<li>Put your json file with grouped scores from the guide, in addition generations folder and metrics |
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folder in it.</li> |
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</ul> |
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<p>The title of the PR should be [Community Submission] Model: org/model, Username: your_username, replace |
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org and model with those corresponding to the model you evaluated.</p> |
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</div> |
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</section> |
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