{"id":627,"date":"2025-01-13T13:22:09","date_gmt":"2025-01-13T13:22:09","guid":{"rendered":"https:\/\/smolagents.org\/?page_id=627"},"modified":"2025-01-13T13:22:11","modified_gmt":"2025-01-13T13:22:11","slug":"big-code-models-leaderboard","status":"publish","type":"page","link":"https:\/\/smolagents.org\/it\/big-code-models-leaderboard\/","title":{"rendered":"Classifica dei modelli di Big Code"},"content":{"rendered":"<p>Inspired from the\u00a0<a href=\"https:\/\/huggingface.co\/spaces\/HuggingFaceH4\/open_llm_leaderboard\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">\ud83e\udd17 Open LLM Leaderboard<\/a>\u00a0e\u00a0<a href=\"https:\/\/huggingface.co\/spaces\/optimum\/llm-perf-leaderboard\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">\ud83e\udd17 Open LLM-Perf Leaderboard \ud83c\udfcb\ufe0f<\/a>, we compare performance of base multilingual code generation models on\u00a0<a href=\"https:\/\/huggingface.co\/datasets\/openai_humaneval\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">HumanEval<\/a>\u00a0benchmark and\u00a0<a href=\"https:\/\/huggingface.co\/datasets\/nuprl\/MultiPL-E\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">MultiPL-E<\/a>. We also measure throughput and provide information about the models. We only compare open pre-trained multilingual code models, that people can start from as base models for their trainings.<\/p>\n\n\n\n<iframe\n\tsrc=\"https:\/\/bigcode-bigcode-models-leaderboard.hf.space\"\n\tframeborder=\"0\"\n\twidth=\"100%\"\n\theight=\"1280px\"\n><\/iframe>\n\n\n\n\n<h2 class=\"wp-block-heading\">What is Big\u00a0Code\u00a0Models\u00a0Leaderboard<\/h2>\n\n\n\n<p>The growing number of code models released by the community necessitates a comprehensive evaluation to reliably benchmark their capabilities. Similar to the\u00a0<a href=\"https:\/\/huggingface.co\/spaces\/HuggingFaceH4\/open_llm_leaderboard\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">\ud83e\udd17 Open LLM Leaderboard<\/a>, we selected two common benchmarks for evaluating Code LLMs on multiple programming languages:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong><a href=\"https:\/\/huggingface.co\/datasets\/openai_humaneval\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">HumanEval<\/a><\/strong>\u00a0&#8211; benchmark for measuring functional correctness for synthesizing programs from docstrings. It consists of 164 Python programming problems.<\/li>\n\n\n\n<li><strong><a href=\"https:\/\/huggingface.co\/datasets\/nuprl\/MultiPL-E\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">MultiPL-E<\/a><\/strong>\u00a0&#8211; Translation of HumanEval to 18 programming languages.<\/li>\n\n\n\n<li><strong>Throughput Measurement<\/strong>\u00a0&#8211; In addition to these benchmarks, we also measure model throughput on a batch size of 1 and 50 to compare their inference speed.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Benchamrks &amp; Prompts<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>HumanEval-Python reports the pass@1 on HumanEval; the rest is from MultiPL-E benchmark.<\/li>\n\n\n\n<li>For all languages, we use the original benchamrk prompts for all models except HumanEval-Python, where we separate base from instruction models. We use the original code completion prompts for HumanEval for all base models, but for Instruction models, we use the Instruction version of HumanEval in\u00a0<a href=\"https:\/\/huggingface.co\/datasets\/bigcode\/humanevalpack\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">HumanEvalSynthesize<\/a>\u00a0delimited by the tokens\/text recommended by the authors of each model (we also use a max generation length of 2048 instead of 512).<\/li>\n<\/ul>\n\n\n\n<p>Figure below shows the example of OctoCoder vs Base HumanEval prompt, you can find the other prompts\u00a0<a href=\"https:\/\/github.com\/bigcode-project\/bigcode-evaluation-harness\/blob\/main\/bigcode_eval\/tasks\/humanevalpack.py\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">qui<\/a>.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"1940\" height=\"724\" src=\"https:\/\/smolagents.org\/wp-content\/uploads\/2025\/01\/humaneval_instruct.png\" alt=\"OctoCoder vs Base HumanEval prompt\" class=\"wp-image-628\" srcset=\"https:\/\/smolagents.org\/wp-content\/uploads\/2025\/01\/humaneval_instruct.png 1940w, https:\/\/smolagents.org\/wp-content\/uploads\/2025\/01\/humaneval_instruct-300x112.png 300w, https:\/\/smolagents.org\/wp-content\/uploads\/2025\/01\/humaneval_instruct-1024x382.png 1024w, https:\/\/smolagents.org\/wp-content\/uploads\/2025\/01\/humaneval_instruct-768x287.png 768w, https:\/\/smolagents.org\/wp-content\/uploads\/2025\/01\/humaneval_instruct-1536x573.png 1536w, https:\/\/smolagents.org\/wp-content\/uploads\/2025\/01\/humaneval_instruct-18x7.png 18w, https:\/\/smolagents.org\/wp-content\/uploads\/2025\/01\/humaneval_instruct-360x134.png 360w\" sizes=\"auto, (max-width: 1940px) 100vw, 1940px\" \/><\/figure>\n\n\n\n<p>&#8211; An exception to this is the Phind models. They seem to follow to base prompts better than the instruction versions. Therefore, following the authors&#8217; recommendation we use base HumanEval prompts without stripping them of the last newline. &#8211; Also note that for WizardCoder-Python-34B-V1.0 &amp; WizardCoder-Python-13B-V1.0 (CodeLLaMa based), we use the HumanEval-Python instruction prompt that the original authors used with their postprocessing (instead of HumanEvalSynthesize), code is available [here](https:\/\/github.com\/bigcode-project\/bigcode-evaluation-harness\/pull\/133)).<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Evaluation Parameters<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>All models were evaluated with the\u00a0<a href=\"https:\/\/github.com\/bigcode-project\/bigcode-evaluation-harness\/tree\/main\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">bigcode-evaluation-harness<\/a>\u00a0with top-p=0.95, temperature=0.2, max_length_generation 512, and n_samples=50.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Throughput and Memory Usage<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Throughputs and peak memory usage are measured using\u00a0<a href=\"https:\/\/github.com\/huggingface\/optimum-benchmark\/tree\/main\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Optimum-Benchmark<\/a>\u00a0which powers\u00a0<a href=\"https:\/\/huggingface.co\/spaces\/optimum\/llm-perf-leaderboard\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">Open LLM-Perf Leaderboard<\/a>. (0 throughput corresponds to OOM).<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Scoring and Rankings<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Average score is the average pass@1 over all languages. For Win Rate, we find model rank for each language and compute\u00a0<code>num_models - (rank -1)<\/code>, then average this result over all languages.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\">Miscellaneous<\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>#Languages column represents the number of programming languages included during the pretraining. UNK means the number of languages is unknown.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">How to submit models\/results to the leaderboard?<\/h2>\n\n\n\n<p>We welcome the community to submit evaluation results of new models. These results will be added as non-verified, the authors are however required to upload their generations in case other members want to check.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">1 &#8211; Running Evaluation<\/h3>\n\n\n\n<p>We wrote a detailed guide for running the evaluation on your model. You can find the it in\u00a0<a href=\"https:\/\/github.com\/bigcode-project\/bigcode-evaluation-harness\/tree\/main\/leaderboard\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">bigcode-evaluation-harness\/leaderboard<\/a>. This will generate a json file summarizing the results, in addition to the raw generations and metric files.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">2- Submitting Results \ud83d\ude80<\/h3>\n\n\n\n<p>To submit your results create a\u00a0<strong>Pull Request<\/strong>\u00a0in the community tab to add them under the\u00a0<a href=\"https:\/\/huggingface.co\/spaces\/bigcode\/multilingual-code-evals\/tree\/main\/community_results\" target=\"_blank\" rel=\"noreferrer noopener nofollow\">folder<\/a>\u00a0<code>community_results<\/code>\u00a0in this repository:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Create a folder called\u00a0<code>ORG_MODELNAME_USERNAME<\/code>\u00a0for example\u00a0<code>bigcode_starcoder_loubnabnl<\/code><\/li>\n\n\n\n<li>Put your json file with grouped scores from the guide, in addition generations folder and metrics folder in it.<\/li>\n<\/ul>\n\n\n\n<p>The title of the PR should be&nbsp;<code>[Community Submission] Model: org\/model, Username: your_username<\/code>, replace org and model with those corresponding to the model you evaluated.<\/p>","protected":false},"excerpt":{"rendered":"<p>Inspired from the\u00a0\ud83e\udd17 Open LLM Leaderboard\u00a0and\u00a0\ud83e\udd17 Open LLM-Perf Leaderboard \ud83c\udfcb\ufe0f, we compare performance of base multilingual code generation models on\u00a0HumanEval\u00a0benchmark and\u00a0MultiPL-E. We also measure throughput and provide information about the models. We only compare open pre-trained multilingual code models, that people can start from as base models for their trainings. What is Big\u00a0Code\u00a0Models\u00a0Leaderboard The growing&#8230;<\/p>","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_kadence_starter_templates_imported_post":false,"_kad_post_transparent":"","_kad_post_title":"","_kad_post_layout":"","_kad_post_sidebar_id":"","_kad_post_content_style":"","_kad_post_vertical_padding":"","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"footnotes":""},"class_list":["post-627","page","type-page","status-publish","hentry"],"rankMath":{"parentDomain":"smolagents.org","noFollowDomains":[],"noFollowExcludeDomains":[],"noFollowExternalLinks":false,"featuredImageNotice":"L&#039;immagine in evidenza dovrebbe essere di almeno 200 x 200 pixel per essere ripresa da Facebook e altri siti di social 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