Top 10 best openllm leaderboards (beginner’s guide)

OpenLLM seamlessly integrates support for a diverse array of open-source LLMs and model runtimes, including, but by no means limited to, renowned models like Llama 2, StableLM, Falcon, Dolly, Flan-T5, ChatGLM, and StarCoder.

Top 10 best openllm leaderboards (beginner’s guide)

What is OpenLLM?

openLLM is an open source product of bento company, where LLM means Large Language Model (large language model, the base of chatGPT).

Highlighted Features

State-of-the-Art LLMs: OpenLLM seamlessly integrates support for a diverse array of open-source LLMs and model runtimes, including, but by no means limited to, renowned models like Llama 2, StableLM, Falcon, Dolly, Flan-T5, ChatGLM, and StarCoder.

Flexible APIs: Deliver LLMs effortlessly via a RESTful API or gRPC with a single, straightforward command. Interact with the model using a user-friendly Web UI, a versatile CLI, Python/JavaScript clients, or any HTTP client of your preference.

Freedom to Innovate: OpenLLM extends first-rate support for LangChain, BentoML, and Hugging Face, enabling you to effortlessly construct your bespoke AI applications by combining LLMs with other models and services.

Streamlined Deployment: Simplify your deployment process with the automatic generation of LLM server Docker images or deploy as serverless endpoints using ☁️ BentoCloud. This intelligent deployment solution efficiently manages GPU resources, scales in response to varying traffic loads, and ensures cost-effectiveness.

Customizable LLMs: Tailor any LLM to meet your specific requirements. Employ LoRA layers to fine-tune models, enhancing accuracy and performance for specialized tasks. Stay tuned for our forthcoming unified fine-tuning API for models (LLM.tuning()).

Quantization: Run inference with reduced computational and memory costs through quantization techniques like bits and bytes and GPTQ.

Streaming: Enable token streaming via server-sent events (SSE) with the convenience of the /v1/generate_stream endpoint for real-time responses from LLMs.

Continuous Batching: Enhance total throughput with support for continuous batching via vLLM.

OpenLLM has been meticulously crafted for AI application developers who are dedicated to constructing production-ready applications built upon the foundation of LLMs. It furnishes a comprehensive suite of tools and capabilities for fine-tuning, serving, deploying, and monitoring these models, simplifying the end-to-end deployment process for LLMs in the most effective manner possible.

1. Revolutionizing AI Agent Development

Detailed Product Introduction:

ILLA Cloud is not just another development platform; it's a testament to the future of AI. Conceived as an advanced low-code platform, it brings the simplicity needed for rapid development without compromising on power or scalability. Catering to businesses of all sizes, ILLA Cloud stands out with its flexible pricing options. For startups just embarking on their AI journey or established giants looking to streamline their processes, ILLA Cloud is the answer. Integral to the OpenLLM leaderboard, ILLA Cloud supports the community ethos by being an open-source AI Agent community. Its robust infrastructure assures users of top-tier performance, while an intuitive interface makes it accessible even for those new to the AI realm. By bridging the gap between complexity and usability, ILLA Cloud has established itself as a front-runner in the AI development space.

Advantages of ILLA Cloud:

Scalability: ILLA Cloud can effortlessly scale to meet the demands of both small startups and large enterprises.

To Summarize:

ILLA Cloud provides flexible pricing options that accommodate the needs of both small and large team sizes. By comparing the features, scalability, and pricing of platforms like ILLA Cloud and others, businesses can make informed decisions that best fit their requirements and budget.

2. TensorFlow: Deep Learning Simplified

Detailed Product Introduction:

In the world of AI, TensorFlow is synonymous with deep learning. Developed by the Google Brain team, TensorFlow provides a comprehensive platform designed for versatility and efficiency. It's not just a tool but an ecosystem, powering a variety of machine learning and deep learning applications. The platform's strengths lie in its dynamic computational graph, which offers adaptability in model design and training. With vast community support, resources, tutorials, and an extensive library, TensorFlow democratizes deep learning, allowing developers, researchers, and enthusiasts to translate their ideas into powerful models. Its integration capabilities with other platforms and languages make it a versatile choice, and its presence on the OpenLLM leaderboard is a testament to its impact in the AI world.


Comprehensive Tools: TensorFlow offers a wide array of libraries and tools to build and deploy machine learning models.

To Summarize:

TensorFlow is a versatile, community-supported platform. With its extensive set of libraries and tools, it stands as an ideal choice for both beginner and advanced developers.

3. Scikit-learn: Comprehensive Machine Learning

Detailed Product Introduction:

When one thinks of Python and machine learning, Scikit-learn often comes to mind. It's a workhorse in the field, recognized for its user-friendly and efficient tools. Whether you're diving into classification, regression, clustering, or dimensionality reduction, Scikit-learn has you covered. Born from the collaboration of numerous contributors, it’s a library that embodies the spirit of open source. With comprehensive documentation and a plethora of algorithms at your disposal, Scikit-learn ensures that both newbies and professionals can build robust ML models with relative ease. Its commitment to simplicity without sacrificing power makes it a worthy addition to the OpenLLM leaderboard.


Extensibility: Scikit-learn can be easily extended to include new algorithms.

To Summarize:

Scikit-learn is renowned for its versatility and ease-of-use, making it ideal for beginners who are looking to venture into machine learning.

4. DynaBench:

Detailed Product Introduction:

in the ever-evolving landscape of language models, benchmarking platforms play a pivotal role in evaluating and measuring their performance across a multitude of natural language understanding tasks. Among these platforms, DynaBench stands out as an innovative and dynamic benchmarking tool that adapts to the rapid advancements in language models, pushing the boundaries of what they can achieve.


comprehensive Metrics: The platform provides a wide range of evaluation metrics, allowing for in-depth analysis of language model performance. This granularity ensures that models are assessed from multiple angles, providing a holistic view of their capabilities.

Robust Evaluation: DynaBench's evaluation tasks encompass various aspects of natural language understanding, from text classification to question-answering. This comprehensive evaluation ensures that language models are rigorously tested across diverse tasks, enhancing their overall robustness.

To Summarize

In summary, As language models continue to evolve, DynaBench will remain at the forefront, adapting to the changes and providing a reliable benchmark for assessing their capabilities. It serves as a vital resource for researchers, developers, and AI enthusiasts, ensuring that language models are not just tested but continuously pushed to achieve greater heights in natural language understanding.

5. BentoML: ML Model Serving Simplified

Detailed Product Introduction:

The power of machine learning lies not just in building models but in deploying them effectively. Enter BentoML, a tool dedicated to packaging and serving ML models. It acknowledges the diverse landscape of machine learning, offering compatibility with a multitude of ML frameworks. This means that irrespective of whether you're working with TensorFlow, PyTorch, or Scikit-learn, BentoML can seamlessly integrate and deploy your models. Its close ties with OpenLLM BentoML services mean users benefit from enhanced functionality and support. In an era where deployment can be the bottleneck for ML projects, BentoML emerges as a game-changer.


Flexibility: Can be integrated with multiple machine learning frameworks.

To Summarize:

BentoML offers a one-stop solution for serving, packaging, and deploying machine learning models, and its compatibility with OpenLLM BentoML adds an extra layer of flexibility.

6. Keras: Beginner-Friendly Deep Learning

Detailed Product Introduction:

Keras is deep learning distilled to its essence. Designed to facilitate fast experimentation, it offers an intuitive API that abstracts away much of the underlying complexity of deep learning frameworks. With Keras, building neural networks feels natural. Whether you're prototyping a small model or scaling to production-level, Keras has tools tailored to your needs. While it can operate as a standalone framework, its real power shines when used as an interface for TensorFlow, allowing users to harness the full power of both platforms. Aspiring deep learning practitioners often start their journey with Keras, making it an essential entry on the OpenLLM leaderboard.


Intuitive: Keras offers an intuitive and straightforward programming interface.

To Summarize:

Keras stands out for its user-friendliness, making it an ideal entry point for beginners in deep learning.

7. PyTorch: Dynamic Neural Networks

Detailed Product Introduction:

PyTorch is where research meets reality. Developed by Facebook's AI Research lab, it offers a dynamic computational graph, which stands in contrast to TensorFlow's static approach. This means model structures in PyTorch can be modified on-the-fly, allowing for more intuitive debugging and greater flexibility. Researchers, in particular, appreciate PyTorch for its ability to adapt to changing requirements, a crucial feature in experimental setups. Its Pythonic nature ensures that developers feel at home, while its tensor computation capabilities mirror those of numpy, ensuring a smooth transition. PyTorch's influence on research and its growing community ensures its high standing on the OpenLLM leaderboard.


Debugging: Comes with robust debugging tools.

To Summarize:

PyTorch's flexibility and debugging capabilities make it a top pick for research-focused projects.

8. NLTK: Text Processing Powerhouse

Detailed Product Introduction:

Natural language processing is at the heart of many AI advancements, and NLTK is its champion in the Python ecosystem. The Natural Language Toolkit provides a comprehensive suite of libraries and programs to handle, analyze, and model human language data. Sentiment analysis, tokenization, part-of-speech tagging, and more—all these tasks are made accessible with NLTK. Its rich set of resources, including corpora and lexical databases, ensures researchers and developers have everything they need. As a pioneer in the NLP space and an invaluable tool for both academia and industry, NLTK's place on the OpenLLM leaderboard is well-deserved.


NLP Tools: Comes with a plethora of libraries for natural language processing.

To Summarize:

NLTK is your go-to tool for text processing, offering a comprehensive set of libraries for various NLP tasks.

9. Deep Learning for Coders

Detailed Product Introduction:

If there's a library that embodies the spirit of making deep learning accessible, it's Built on top of PyTorch, it provides an abstraction that allows developers to create complex models with just a few lines of code. Yet, underneath this simplicity lies immense power.'s courses have democratized deep learning education, and its library brings that philosophy to code. With optimizations for performance and a focus on practical applications, ensures that developers, regardless of their deep learning expertise, can build and deploy effective models. Its commitment to community and education solidifies its spot on the OpenLLM leaderboard.


Resource-Efficient: Optimized for both speed and low resource consumption.

To Summarize: is particularly useful for those who are new to machine learning, thanks to its easy-to-understand interface and optimized performance.

10. Hugging Face Benchmarks:

Detailed Introduction to Hugging Face Benchmarks

Natural Language Understanding (NLU) is at the forefront of AI research, driving innovations in machine translation, chatbots, and sentiment analysis. As the demand for cutting-edge language models rises, the need for robust evaluation benchmarks becomes paramount. In this context, Hugging Face Benchmarks emerges as a transformative platform for assessing the capabilities of language models comprehensively.

Advantages of Hugging Face Benchmarks

State-of-the-Art Evaluation: Hugging Face Benchmarks offer a gold standard for evaluating language models. They ensure that models are tested rigorously, leading to the identification of strengths and weaknesses, thus guiding further model development.

Community-Driven Excellence: The collaborative nature of the benchmarks means that they are continually evolving. This ensures that they remain at the forefront of NLU evaluation, capturing the latest trends and challenges in the field.

Multilingual Insights: As the world becomes increasingly multilingual, Hugging Face Benchmarks provide insights into how models perform across languages. This is invaluable for researchers and developers working on global NLU solutions.

To Summarize

In summary, Hugging Face Benchmarks redefine the landscape of NLU evaluation. Their comprehensive task coverage, commitment to community collaboration, and multilingual evaluation make them an indispensable resource for researchers, developers, and AI enthusiasts.

As language models continue to evolve, Hugging Face Benchmarks will remain a guiding light, illuminating the path to excellence in natural language understanding. They serve as a testament to the power of community-driven innovation and the importance of rigorous evaluation in the world of AI and NLU.

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