Llm benchmark leaderboard. Aug 17, 2023 · Agent Bench AI benchmark tool demo.

Hugging Face’s leaderboard, along with resources like lmsys. What is the "HF Open LLM Leaderboard"? It is a platform where users can submit their models for automated evaluation on a GPU cluster, making the Hugging Face LLM Leaderboard a hub for innovation and development in AI. * EQ-Bench v2 scoring system has superseded v1. We use GPT-4 to grade model responses. The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems. LLM Leaderboard best models ️‍🔥. Currently, the performance of LLaMA 65B on Open LLM Leaderboard is just 48. AI training and optimization leader Hugging Face has released its second LLM leaderboard, with a host of new and edited trials to put LLMs through their paces. org’s Chatbot Arena, ranks among the most trusted resources for evaluating open-source LLMs. Enter Jul 6, 2024 · Unpacking LLM Benchmarks: A Guide (Part 2) The landscape of LLM evaluation is undergoing a rapid and necessary evolution -from Hugging Face's open LLM leaderboard to Salesforce's industry-specific CRM benchmark, we are witnessing a shift. We use 1. This leaderboard, a vital resource for developers, AI researchers, and enthusiasts, showcases the cutting-edge of LLM technology. LLM-Performance-Leaderboard. yaml」を参照してください。 6. For works that have used MTEB for benchmarking, you can find them on the leaderboard. bigcode-models-leaderboard. The main content of this repository is plaintext files containing detailed benchmark results. Jun 24, 2024 · MMLU is a comprehensive benchmark designed to evaluate an LLM’s natural language understanding (NLU) and problem-solving abilities across diverse subjects. 6). Close. Over a four-part series, we’ll dig into each of these benchmarks to get a sense of what exactly Hugging Face’s Open LLM Leaderboard aims to evaluate and learn about what goes into designing challenging LLM May 3, 2023 · We present Chatbot Arena, a benchmark platform for large language models (LLMs) that features anonymous, randomized battles in a crowdsourced manner. While performance on individual use The General Language Understanding Evaluation (GLUE) benchmark is a collection of resources for training, evaluating, and analyzing natural language understanding systems. PaLM: Scaling Language Modeling with Pathways. In this blog post, we are releasing our initial results and a leaderboard based on the Elo rating system, which is a widely-used rating system in chess and other competitive games. The most popular open-source LLMs for coding are Code Llama, WizardCoder, Phind-CodeLlama, Mistral, StarCoder, & Llama 2. When making decisions regarding which AI technologies to use, engineers need to consider quality, price and speed (latency & throughput). , multiple choices), which do not reflect the typical use cases of LLM-based chat assistants. An extensible game simulation software to test LLMs via games such as Tic-Tac-Toe, Connect Four, and Gomoku. See a full comparison of 135 papers with code. Open-LLM-Leaderboard: From Multi-choice to Openstyle Questions for LLMs Evaluation, Benchmark, and Arena Aidar Myrzakhan *, Sondos Mahmoud Bsharat *, Zhiqiang Shen * * joint first author & equal contribution. If you are interested in the sources of each individual reported model value, please visit the llm-leaderboard repository. like 211. py -h # to list all supported arguments python eval. 0) by the provided GPT-4 based Explore the community-made ML apps and see how they rank on the C-MTEB benchmark, a challenging natural language understanding task. We explain all these options in more detail below. HellaSwag. 4M+ user votes to compute Elo ratings. To evaluate the ability of LLMs on code, both academic and industry practitioners rely on popular handcrafted benchmarks. Jul 06, 2024. Aug 17, 2023 · Agent Bench AI benchmark tool demo. 7-DPO and is ultimately based on Qwen-72B. Triangulating relative model performance with MT-Bench and AlpacaEval provides the best benchmark for general performance from both human preference and LLM-as-judge perspectives. Jun 3, 2024 · Hugging Face Open LLM Leaderboard. , 0. Leaderboard. The format We present Platypus a family of fine-tuned and merged Large Language Models (LLMs) that achieves the strongest performance and currently stands at first place in HuggingFace's Open LLM Leaderboard as of the release date of this work. We created Smaug-72B-v0. In this update, we have added 4 new yet strong players into the Arena, including three proprietary models and one open-source Aug 15, 2023 · Hugging Face’s four choice benchmarks are: AI2 Reasoning Challenge. Key Objectives of LLM benchmarks. I am interested in how more experienced people here evaluate an LLM's fitness. KP. To fundamentally eliminate selection bias and random guessing in LLMs, in this work, we build an open-style question benchmark for LLM evaluation. 8. Our aim is to provide users and developers with vital insights into the capabilities and limitations of each provider, informing decisions for future integrations and deployments. 4 reported in the paper. e. Due to concerns of contamination and leaks in the test dataset, I have determined that the rankings on Hugging Face's Open LLM Leaderboard can no longer be fully trusted. The Index ranks 11 leading LLMs performance across three task types. However, prior benchmarks contain only a very limited set of problems, both in quantity and variety See how different open large language models perform in chatbot arena. Massive Multitask Language Understanding. It outperforms models with up to 30B parameters, even surpassing the recent Mixtral 8X7B model. Smaug-72B is finetuned directly from moreh/MoMo-72B-lora-1. Supporting seven Indic languages, it offers a comprehensive platform for assessing model performance and comparing results within the Indic language modeling landscape. The Open LLM Leaderboard ranks models on several benchmarks including ARC, HellaSwag and MMLU, and makes it possible to filter models by type, precision, architecture, and other options. 1, Math 0-shot: 32. LLM leaderboards test language models by putting them through standardized benchmarks backed by detailed methods and large databases. We are actively iterating on the design of the arena and leaderboard scores. The leaderboard enables interactive model comparisons and traces back to the original experiments. The leaderboard contains information about each submission, as well as the scores for the subtasks included within the SuperGLUE benchmark. These files provide a comprehensive record of the performance characteristics of different LLMs under various conditions and workloads. TruthfulQA. com. Different from previous works that rely on human evaluation or thousands of crowd users on Chatbot Arena, we can have a benchmark for chat LLMs in a fast, automatic, and cheap scheme. In particular, the US Whitehouse has published an executive order on safe, secure, and trustworthy AI; the EU AI Act has emphasized The Nejumi LLM Leaderboard Neo employs a rigorous zero-shot evaluation method with W&B's Table feature, allowing in-depth analysis of each question. Benchmarking Truthfulness: TruthfulQA. LLM-Eval offers a versatile and robust solution for evaluating open-domain conversation systems, streamlining the evaluation process and providing consistent performance across diverse scenarios. It boosts competition, aids in model development, and sets a standard for measuring model effectiveness across tasks such as text generation Compare and rank large language models (LLMs) based on Chatbot Arena, MT-Bench, MMLU, Coder EvalPlus, Text2SQL, and OpenCompass. Adversarial robustness; DyVal benchmark; Prompt Engineering benchmark; Code. Solar 10. おわりに 「Nejumi LLMリーダーボード Neo」の「LLMベンチマーク」を使うことで、日本語LLMの能力評価を簡単に行うことができることを紹介しまし Jun 26, 2024 · The Open LLM Leaderboard, a benchmark tool that has become a touchstone for measuring progress in AI language models, has been retooled to provide more rigorous and nuanced evaluations. These responses are then compared to reference responses (Davinci003 for AlpacaEval, GPT-4 Preview for AlpacaEval 2. Jun 11, 2024 · We proposed Open-LLM-Leaderboard for LLM evaluation and comprehensively examined its efficacy using open-style questions from nine datasets on OSQ-bench. 78) and mathematics (GSM8K 0-shot: 84. lmsys. Dec 21, 2023 · The LLMPerf Leaderboard ranks LLM inference providers based on a suite of key performance metrics and gets updated weekly. † MAGI-Hard is a custom subset of MMLU and AGIEval which is highly discriminative amongst the top models (and weakly discriminative lower down). The chart helps visualize the distributions of different speeds, as they can vary somewhat depending on the loads. For most applications, an untruthful LLM probably isn’t very useful to us (though it may be helpful for creative applications). Providing broad coverage and recognizing incompleteness, multi-metric measurements, and standardization. Nov 2, 2023 · Yi-34B model ranked first among all existing open-source models (such as Falcon-180B, Llama-70B, Claude) in both English and Chinese on various benchmarks, including Hugging Face Open LLM Leaderboard (pre-trained) and C-Eval (based on data available up to November 2023). Sources say it employs multiple "external AI agents" to perform specific tasks, meaning it should be capable of reliably solving complex 3 days ago · The leaderboard serves as a crucial tool for evaluating and choosing the most suitable LLM provider for various applications and budget considerations. With the growth of ChatGPT, new LLM cloud services have been launched by familiar incumbents as well as well-capitalized startups. For detailed information, please refer to the experimental table. This benchmark includes a selection of LLM inference providers and the analysis focuses on evaluating for performance, reliability, and efficiency The Language Model (LLM) Leaderboard, a critical ranking system in the natural language processing (NLP) field, plays a crucial role in evaluating and comparing the performance of diverse language models. Kenneth Enevoldsen, Márton Kardos, Niklas Muennighoff, Kristoffer Laigaard Nielbo. To settle the case, we decided to run these three possible implementations of the same MMLU evaluation on a set of models to rank them according to these results: The LLMPerf Leaderboard displays results in a clear, transparent manner. 28k. 2. 3% with 10-shot reasoning. The Berkeley Function Calling Leaderboard (also called Berkeley Tool Calling Leaderboard) evaluates the LLM's ability to call functions (aka tools) accurately. 7B has remarkable performance. Leveraging this benchmark, we present the Open-LLM-Leaderboard, a new automated framework designed to refine the assessment process of LLMs. Jun 28, 2024 · Leaderboard Comparing LLM Performance at Producing Hallucinations when Summarizing Short Documents vectara. Read more about SWE-bench in our paper ! The leaderboards below report the results from a number of popular LLMs. GPT4, Claude) correlate better with human score than metric-based eval measures. Enter. This AI I run cron jobs to periodically test the token generation speed of different cloud LLM providers. VILA Lab, Mohamed bin Zayed University of AI (MBZUAI) May 10, 2023 · We release an updated leaderboard with more models and new data we collected last week, after the announcement of the anonymous Chatbot Arena. SWE-bench is a dataset that tests systems' ability to solve GitHub issues automatically. May 29, 2024 · The SEAL Leaderboards are a set of LLM model rankings across a number of popular public models, based upon curated private datasets that can’t be gamed, all funded and developed by Scale. The scoring methodology is explained below. Leaderboard rank and results are not a guarantee of the associated LLM’s accuracy, performance or reliability. Compare their Elo ratings and chat quality on the leaderboard. 91. This benchmark measures the We’re thrilled to announce that Groq is now on the LLMPerf Leaderboard by Anyscale, a developer innovator and friendly competitor in the Large Language Model (LLM) inference benchmark space. We provide a toolkit to facilitate evaluations by others, and you can submit the results of your own large language models online. In the rapidly evolving world of generative AI, benchmarks serve as crucial yardsticks This leaderboard is based on the following three benchmarks. This Explore the world of Zhihu columns, where you can freely express yourself and write to your heart's content. I guess there is something similar going on like it used to with GPU benchmarks: dirty tricks and over-optimization for the tests. 50. Its website also provides a starting toolkit for quickly evaluating models on the benchmark. Jun 22, 2023 · Traditional benchmarks often test LLMs on close-ended questions with concise outputs (e. Traditional benchmarks struggle to automatically evaluate response quality. This benchmark helps developers understand the strengths and weaknesses of different models, guiding the selection process for specific applications. Our Partners. • The model's memory footprint includes 4-bit weights and KV cache at full context length (factor in extra for process overhead, library code, ect) Jan 24, 2024 · 具体的な例は、「llm-leaderboard」のwandbプロジェクトのArtifactで公開されている「config. Jul 27, 2023 · The first one is HuggingFace – OpenLLM benchmark Open LLM Leaderboard – a Hugging Face Space by HuggingFaceH4 which uses some specific benchmarks to evaluate LLMs from a score from 0 – 100 and mostly based upon the GitHub – EleutherAI/lm-evaluation-harness: A framework for few-shot evaluation of autoregressive language models. Introducing LiveBench: a benchmark for LLMs designed with test set contamination and objective evaluation in mind. updated about 1 month ago. Leaders. More info. 353. Top-shelve LLM (e. "The Scandinavian Embedding Benchmarks: Comprehensive Assessment of Multilingual and Monolingual Text Embedding" arXiv 2024. Jun 16, 2024 · Indic LLM Leaderboard: Utilizes the indic_eval evaluation framework, incorporating state-of-the-art translated benchmarks like ARC, Hellaswag, MMLU, among others. 1 using a new fine-tuning technique, DPO Feb 26, 2024 · They provide a standardized method to evaluate LLMs across tasks like coding, reasoning, math, truthfulness, and more. Scores are not directly comparable between v1 and v2. License. Introduction. Built with Gradio. You can also check the leaderboard and see how the models rank against each other. Given the widespread adoption of LLMs, it is critical to understand their safety and risks in different scenarios before extensive deployments in the real world. 7B is an ideal choice for fine-tuning. AgentBench, is a remarkable new benchmarking tool designed specifically for evaluating the performance and accuracy of Language Learning Models (LLM). Running A leaderboard of current model performance on BBL is shown below. 冗長な回答を高く評価しやすいことや、評価者となったLLMに似た回答を高く評価しやすいのが欠点。 「Nejumi LLMリーダーボード 3」「日本語LLM評価」「shaberiベンチマーク」には「一問一答のベンチマーク」も含まれます。 LMSys Chatbot Arena Leaderboard - a Hugging Face Space by lmsys. Aug 9, 2023 · To this day, the leaderboard is still active with submissions and improvements. MT-Bench - a set of challenging multi-turn questions. To add new model results to the full BIG-bench leaderboard, to the BBL leaderboard, and to individual task performance plots, open a PR which includes the score files generated when you evaluate your model on BIG-bench tasks. It comprises 15,908 questions divided into 57 tasks, covering STEM, humanities, social sciences, and other topics from elementary to professional levels. Taking into consideration aspects such as We introduce the Open-LLM-Leaderboard to track various LLMs’ performance on open-style questions and reflect their true capability. Loading Use via API. Explore and use various benchmarks and tools for LLM evaluation and research. 6. We consider most leading models. SOLAR-10. Most software businesses are familiar with cloud service providers (CSPs) that provide scalable computing resources. May 3, 2023 · The results of this leaderboard are collected from the individual papers and published results of the model authors. 96 correlation with Chatbot Arena) while running locally and quickly (6% the time and cost of running MMLU), with its queries being stably and effortlessly updated Are you interested in chatting with open large language models (LLMs) and comparing their performance? Join the Chatbot Arena, a platform where you can interact with different LLMs and vote for the best one. 1 which has taken first place on the Open LLM Leaderboard by HuggingFace. like 353. 0 license . Aug 20, 2023 · 3. The All-in-One LLM Metric: SuperGLUE To review the current status of the leaderboard, please see the leaderboard folder. LiveBench has the following properties: LiveBench is designed to limit potential contamination by releasing new questions monthly, as well as having questions based on recently-released datasets, arXiv papers, news Which is often lacking in LLMs I am trying so far, like the boards leader for 30B models 01-ai/Yi-34B for example. like. Compared to existing benchmarks and community driven approaches, we place a high emphasis on: Leaderboard Integrity1: Unlike most public benchmarks, Scale's We recently released Smaug-72B-v0. SLM Benchmarks • The HuggingFace Open LLM Leaderboard is a collection of multitask benchmarks including reasoning & comprehension, math, coding, history, geography, ect. The LLMs were evaluated using seven popular datasets. In this work we describe (1) our curated dataset Open-Platypus, that is a subset of other open datasets and This is the reason the Open LLM Leaderboard is wrapping such “holistic” benchmarks instead of using individual code bases for each evaluation. It is the first open-source model to surpass an average score of 80%. The Open LLM Leaderboard provides a comprehensive platform to compare the performance of LLMs based on metrics like accuracy, speed, and versatility. Honesty is an admirable trait—in humans and LLMs. Code editing leaderboard Aider’s code editing benchmark asks the LLM to edit python source files to complete 133 small coding exercises. Benchmark results are for information only and not advice or recommendations. cd eval python eval. The updates for the Open LLM LeaderBoard Report(This Repository) will officially cease on November 13, 2023. LLAMA2, LLAMA1, and T5, based on our scoring methodology, these models scored 89, 87, and 81 points, respectively. The current state-of-the-art on HumanEval is LDB (GPT-4o, based on seed programs from Reflexion). This leaderboard consists of real-world data and will be updated periodically. org - Chatbot Arena benchmarks. GLUE consists of: A public leaderboard for tracking performance on the benchmark and a dashboard for visualizing the performance of models on the diagnostic set. Special thanks to the following pages: MosaicML - Model benchmarks. 3. Emotional Intelligence Benchmark for LLMs. Superior General Capabilities: DeepSeek LLM 67B Base outperforms Llama2 70B Base in areas such as reasoning, coding, math, and Chinese comprehension. Variants of Alibaba's Qwen LLM hold Mar 28, 2024 · LLMs have become the go-to choice for code generation tasks, with an exponential increase in the training, development, and usage of LLMs specifically for code generation. Variants of Alibaba's Qwen LLM hold Jun 27, 2024 · AI training and optimization leader Hugging Face has released its second LLM leaderboard, with a host of new and edited trials to put LLMs through their paces. About VMLU. PaLM 8B. Jun 27, 2024 · HumanEval: Decoding the LLM Benchmark for Code Generation. 8, which is significantly lower than the 63. LLMを評価者としたベンチマーク. By comparing different models, benchmarks highlight their strengths and weaknesses. Dec 1, 2023 · We added it to the Open LLM Leaderboard three weeks ago, and observed that the f1-scores of pretrained models followed an unexpected trend: when we plotted DROP scores against the leaderboard original average (of ARC, HellaSwag, TruthfulQA and MMLU), which is a reasonable proxy for overall model performance, we expected DROP scores to be correlated with it (with better models having better Compare the capabilities, price and context window of leading commercial and open-source LLMs based on benchmark data in 2024. In line with our commitment to transparency and utility, we also provide reproducible steps Apr 9, 2024 · EQ-Bench. Those leaderboards are capable of outlining a structured methodology for assessing how each model performs relative to others. 2022. So, those high-accuracy correlations made us wonder… Could we create accurate LLM benchmarks using only a fraction of the tests’ datasets? Mar 16, 2024 · MMLU (Massive Multitask Language Understanding): A wide-ranging benchmark suite designed to push LLMs beyond the basics. g. To fill this gap, in this leaderboard update, in addition to the Chatbot Arena Elo system, we add a new benchmark: MT-Bench. Jun 27, 2024 · AI training and optimization leader Hugging Face has released its second LLM leaderboard, with a host of new and edited trials to put LLMs through their paces. This casts doubts on the comparison between LLaMA and Falcon. They are produced using methodologies incorporating AI tools and provided “as-is” without any express or implied warranties by S&P and its affiliates llm-perf-leaderboard. In the era of artificial intelligence and machine learning, evaluating the performance of models is crucial for their development and improvement. MMLU aims for a comprehensive evaluation. These metrics are generated by the open source LLMPerf tool, using a representative use case scenario of 550 input tokens and 150 output tokens. 🙏 (Credits to Llama) Thanks to the Transformer and Llama open-source Feb 6, 2024 · Each file in eval/models contains an evaluator specified to one M/LLM, and implements a generate_answer method to receive a question as input and give out the answer of it. Jan 26, 2024 · An Introduction to AI Secure LLM Safety Leaderboard. The Big Benchmarks Collection. May 19, 2024 · Since HellaSwag was released in 2019, a non-trivial gap remains between humans, who score around 95%, and Falcon-40b, the open LLM leader on Hugging Face’s Leaderboard (as of July 4, 2023), which scores 85. We outline a few key properties that an LLM chatbot benchmark should possess to provide a meaningful measurement of capabilities between models: 89. Running App Files Files Community 28 Refreshing Comparison and ranking the performance of over 30 AI models (LLMs) across key metrics including quality, price, performance and speed (output speed - tokens per second & latency - TTFT), context window & others. 3%. Useful leaderboard tools. In the future, I may introduce a "leaderboard" feature, which will rank the LLMs based on their benchmark performance. While aider can connect to almost any LLM, it works best with models that score well on the benchmarks. py -l # to list all supported models Regular Updates — The leaderboard is updated regularly, providing a constantly evolving view of the latest LLM performance. 7B offers robustness and adaptability for your fine-tuning needs. Jun 1, 2024 · TL;DR: We introduce MixEval, a ground-truth-based dynamic benchmark derived from off-the-shelf benchmark mixtures, which evaluates LLMs with a highly capable model ranking (i. Read more here. Additionally, we strongly discourage the use of the test set as training data to enhance the model's performance, as this would significantly impede the progress of the field. The current leader LLAMA2 is a collection of pretrained and fine-tuned large language models (LLMs) that range in scale from 7 billion to 70 billion parameters. Closed-source LLMs, however, are now performing on par with humans, with GPT-4 scoring 95. Feb 20, 2024 · Ko-CommonGEN V2: A newly made benchmark for the Open Ko-LLM Leaderboard assesses whether LLMs can generate outputs that align with Korean common sense given certain conditions, testing the model’s capacity to produce contextually and culturally relevant outputs in the Korean language. Open LLM Leaderboard evaluates open-sourced language models. The Holistic Evaluation of Language Models (HELM) serves as a living benchmark for transparency in language models. Dec 25, 2023 · Chatbot Arena: Benchmarking LLM assistants proves challenging due to the open-ended nature of chatbot problems. Understanding the Klu Index Score The Klu Index Score evaluates frontier models on accuracy, evaluations, human preference, and performance. For readability not all models are shown, but you can see the full results in the table below. All data and analysis are freely accessible on the website for exploration and study. Proficient in Coding and Math: DeepSeek LLM 67B Chat exhibits outstanding performance in coding (HumanEval Pass@1: 73. Running. Discover amazing ML apps made by the community. To measure hallucinations, the Hallucination Index employs two metrics, Correctness and Context Adherence, which are built with the state-of-the-art evaluation method ChainPoll. It features over 15,000 questions across 57 diverse tasks, spanning STEM subjects, humanities, and other areas of knowledge. They tackle a range of tasks such as text Apr 19, 2024 · Full leaderboard at the Result section: Skip; We explain more technical details in the following sections. 873. See how Claude 3, GPT-4, Llama 3 and other models perform on various tasks such as multi-choice, reasoning, coding, math and more. The leaderboard is available for viewing on HuggingFace. May 13, 2024 · May 13, 2024. A daily uploaded list of models with best evaluations on the LLM leaderboard: Upvote. For more information and try out the game simulations, please see the game simulation folder. 5, Claude 2, & Palm 2. Evaluation is performed by unit test verification using post-PR behavior as the reference solution. AlpacaEval an LLM-based automatic evaluation that is fast, cheap, and reliable. Variants of Alibaba's Qwen LLM hold Oct 19, 2023 · As of October 2023, the most popular commercial LLMs for coding are GPT-4, GPT-3. This comprehensive benchmark comprises 10,880 multiple-choice questions covering 58 distinct subjects, distributed across four overarching domains: STEM SOLAR-10. ·. It is based on the AlpacaFarm evaluation set, which tests the ability of models to follow general user instructions. These metrics are chosen on a best-effort basis to be as fair, transparent May 5, 2023 · Open LLM Leaderboard (Hugging Face) Commercial LLMs. Below we provide you with an introduction to the benchmarks that the creators of these models used in their papers as Jun 4, 2023 · 中文大模型能力评测榜单：覆盖百度文心一言、chatgpt、阿里通义千问、讯飞星火、belle / chatglm6b 等开源大模型，多维度能力评测。 Jun 6, 2024 · These benchmarks help compare strengths and weaknesses of different models across varied tasks and scenarios. We will add a * in the leaderboard By comparing the performance of myriad large language models against a set of predetermined benchmarks or tasks, an LLM leaderboard stands as a vital evaluative framework. You can use OSQ-bench questions and prompts to evaluate your models automatically with an LLM-based evaluator. Chatbot Arena - a crowdsourced, randomized battle platform for large language models (LLMs). Large Language Models (LLMs) have shown incredible capabilities in generating human-like text, and their application has been extended to May 3, 2024 · The LLM Performance Leaderboard aims to provide comprehensive metrics to help AI engineers make decisions on which LLMs (both open & proprietary) and API providers to use in AI-enabled applications. VMLU is a human-centric benchmark suite specifically designed to assess the overall capabilities of foundation models, with a particular focus on the Vietnamese language. Apache-2. Installation; Basic Evaluation Pipeline; DyVal Evaluation; Prompt Attack; LLM enhancement ; This site uses Just the Docs, a documentation theme for Jekyll. May 19, 2024 · We’re now ready for the final benchmark of this series (and on the Hugging Face’s Open LLM Leaderboard), TruthfulQA. Below we share more information on the current LLM benchmarks, their limits, and how various models stack up. The dataset collects 2,294 Issue-Pull Request pairs from 12 popular Python repositories. Mar 27, 2024 · The new LLM model is leaps and bounds better than GPT-4. pv nb vj te og vt nw zg zk fp