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Best 25 Open Source Large Language Models of 2024

Best 25 Open Source Large Language Models of 2024

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Want to know the best Open Source LLM to test out? Read this article to find out now!

Introduction to Open Source LLMs

The landscape of open source large language models (LLMs) has expanded significantly in 2024, offering researchers, developers, and businesses access to state-of-the-art models without the need for proprietary licenses. This article explores over 20 of the top open source LLMs, their key features, benchmarks, best use cases, number of parameters, and context length.

Why Open Source LLMs are Better

Open source LLMs offer several compelling advantages over their proprietary counterparts, making them an increasingly attractive choice for a wide range of applications. Here are some key reasons why open source LLMs are better:

  • Cost-effectiveness: Open source LLMs are freely available, eliminating the need for expensive licensing fees associated with proprietary models. This makes them more accessible to researchers, startups, and organizations with limited budgets.

  • Transparency: The open nature of these models allows for greater transparency into their architecture, training data, and inner workings. This transparency fosters trust, enables auditing, and facilitates reproducibility of results.

  • Customization and flexibility: Open source LLMs provide the freedom to modify, adapt, and fine-tune the models to suit specific use cases and domain requirements. This flexibility is crucial for organizations looking to build tailored AI solutions.

  • Community-driven innovation: Open source LLMs benefit from the collective intelligence and contributions of a global community of researchers and developers. This collaborative approach accelerates innovation, leading to rapid improvements and diverse applications.

  • Mitigating vendor lock-in: By opting for open source LLMs, organizations can avoid being locked into a single vendor's ecosystem. This independence allows for greater control over data, infrastructure, and the ability to switch between models as needed.

  • Addressing ethical concerns: The transparency and accountability afforded by open source LLMs help address ethical concerns surrounding AI, such as bias, fairness, and responsible use. The ability to inspect and modify these models enables researchers to identify and mitigate potential issues.

While proprietary LLMs still have their place, particularly in scenarios requiring enterprise-grade support and seamless integration, the benefits of open source LLMs are compelling. As the open source LLM ecosystem continues to mature, we can expect to see even more powerful and versatile models that rival or surpass their proprietary counterparts.

Top 25 Open Source LLMs

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1. Mistral

Mistral 7B is an open source LLM developed by Mistral AI, showing promising performance and supporting long context lengths.

Key features of Mistral 7B include:

  • Competitive performance on language modeling and downstream tasks
  • Long context length of 4096-16K tokens using sliding window attention
  • Released under the Apache 2.0 license

Mistral 7B's long context length makes it suitable for tasks involving extended text, such as document summarization, long-form question answering, and context-aware generation. Its sliding window attention allows for efficient processing of very long input sequences.

Further Readings about Mistral Models:

2. OpenHermes

OpenHermes is a series of open source LLMs developed by Nous Research, with sizes ranging from 2.5B to 13B parameters.

Key features of OpenHermes models include:

  • Strong performance on language modeling and downstream tasks
  • Efficient training and inference using the Triton language and compiler
  • Released under the Apache 2.0 license

OpenHermes models are versatile and can be used for a variety of language understanding and generation tasks. Their efficient training and inference make them suitable for resource-constrained environments or applications with strict latency requirements.

3. LLaMA 2

Meta's LLaMA 2 family of models, released in June 2023, aims to democratize access to powerful language models with sizes ranging from 7B to 70B parameters.

Key features of LLaMA 2 models include:

  • Competitive performance on language modeling and downstream NLP tasks
  • Long context length of 4096 tokens, enabling better understanding of extended text
  • Flexibility in deployment with a range of model sizes
  • Released under a custom license, allowing free use for entities with under 700M users, with some restrictions

LLaMA 2 models have found applications in content generation, summarization, dialogue systems, and question answering. Their strong performance and open source nature have made them a popular choice for researchers and developers.

4. Bloom

Bloom, developed by BigScience, is a 176B parameter open access multilingual language model that has gained significant adoption since its release in 2022.

Key features of Bloom include:

  • Strong performance across a range of NLP tasks and benchmarks, particularly in multilingual settings
  • Multilinguality, supporting text generation in 46 languages and 13 programming languages
  • Released under the OpenRAIL-M v1 license, allowing for flexible usage and modification

Bloom's multilinguality and strong performance make it a compelling choice for applications serving diverse linguistic audiences. It is well-suited for tasks like translation, multilingual content generation, and cross-lingual understanding.

5. OPT

OPT (Open Pre-trained Transformer) is a series of open source LLMs ranging from 125M to 175B parameters, developed by Meta AI.

Key features of OPT models include:

  • Strong zero-shot performance on various NLP benchmarks
  • Training on a large corpus of unlabeled text data
  • Flexibility in deployment with a range of model sizes
  • Released under the Apache 2.0 license

OPT's strong zero-shot capabilities make it suitable for applications where fine-tuning is not feasible. The range of model sizes allows for flexible deployment across different compute budgets and latency requirements.

6. GPT-NeoX-20B

GPT-NeoX-20B is an open source autoregressive language model with 20 billion parameters, developed by EleutherAI.

Key features of GPT-NeoX-20B include:

  • Competitive performance on language modeling benchmarks
  • Promising few-shot learning capabilities
  • Released under the Apache 2.0 license

GPT-NeoX-20B is well-suited for generative tasks like story writing, article generation, and creative writing. Its strong language modeling capabilities make it a good choice for applications requiring coherent text generation.

7. Pythia

Pythia is a suite of open source LLMs ranging from 70M to 12B parameters, aimed at enabling analysis of language models across training and scaling.

Key features of Pythia models include:

  • Promising performance on various NLP tasks
  • Designed to facilitate research into the training dynamics and scaling properties of language models
  • Released under the Apache 2.0 license

Pythia models are primarily intended for research purposes, allowing for controlled experiments on the effects of model scale, training data, and hyperparameters. They can also be used as base models for fine-tuning on specific downstream tasks.

8. OpenLLaMA

OpenLLaMA is an open reproduction of Meta's LLaMA models, with sizes ranging from 3B to 13B parameters.

Key features of OpenLLaMA models include:

  • Faithful reproduction of LLaMA's architecture and training methodology
  • Enabling researchers to study and build upon state-of-the-art language models
  • Released under the Apache 2.0 license

OpenLLaMA models are valuable for research into language model architectures, training techniques, and scaling laws. They can also serve as a starting point for developing derivative models tailored to specific domains or tasks.

9. OLMo

Developed by the Allen Institute for AI (AI2), OLMo (Open Language Model) is a family of open source LLMs that prioritize transparency, reproducibility, and accessibility. The largest model, OLMo 7B Twin 2T, demonstrates impressive performance on a range of NLP benchmarks.

Key features of OLMo models include:

  • Training on a diverse corpus of high-quality text data
  • Emphasis on reproducibility, with detailed documentation and open source training code
  • Released under the Apache 2.0 license

OLMo models are well-suited for research applications, with a focus on interpretability and robustness. They can be used for a variety of language understanding and generation tasks.

10. Gemma

Gemma is a family of open source LLMs developed by Google, with unique features like support for long-range context up to 8192 tokens.

Key features of Gemma models include:

  • Competitive performance on language modeling and downstream NLP benchmarks
  • Efficient training and inference using Google's JAX framework
  • Multilingual variants, such as Gemma 7B it, trained on Italian text data
  • Released under the Gemma Terms of Use, allowing for flexible usage and modification

Gemma's long context length makes it particularly well-suited for tasks involving extended text, such as document summarization, long-form question answering, and content generation. Its multilingual variants are valuable for language-specific applications.

11. GPT-J-6B

GPT-J-6B is a 6 billion parameter open source language model developed by EleutherAI.

Key features of GPT-J-6B include:

  • Widely used and strong performance on various language tasks
  • Serves as a foundation for many derivative models and applications
  • Released under the Apache 2.0 license

GPT-J-6B is a versatile model suitable for a range of language generation and understanding tasks. Its moderate size makes it more accessible for deployment compared to larger models.

12. Dolly

Dolly is a series of instruction-tuned open source LLMs developed by Databricks, with sizes from 3B to 12B parameters.

Key features of Dolly models include:

  • Strong performance on instruction-following tasks and general language understanding
  • Based on the Pythia architecture
  • Used for building chatbots and other applications
  • Released under the MIT license

Dolly's instruction-tuning makes it well-suited for building conversational agents, task-oriented dialogue systems, and applications that require following specific instructions. The range of model sizes allows for flexibility in deployment.

13. StableLM-Alpha

StableLM-Alpha is a suite of open source LLMs ranging from 3B to 65B parameters, developed by Stability AI.

Key features of StableLM-Alpha models include:

  • Promising performance on language modeling and downstream tasks
  • Long context length of 4096 tokens, enabling better understanding of extended text
  • Released under the CC BY-SA-4.0 license

StableLM-Alpha's long context length makes it suitable for tasks involving longer input sequences, such as document understanding, summarization, and context-aware generation. The range of model sizes allows for flexibility in deployment.

14. RWKV

RWKV is a family of open source RNN-based language models with sizes up to 14B parameters.

Key features of RWKV models include:

  • Transformer-level performance while having O(1) inference time independent of context length
  • Infinite context length (RNN-based)
  • Strong results on language modeling and downstream tasks
  • Released under the Apache 2.0 license

RWKV's infinite context length and efficient inference make it well-suited for tasks involving very long input sequences or real-time generation. It is a good choice for applications that require processing long documents or maintaining long-term context.

15. FastChat-T5

FastChat-T5 is a 3B parameter open source chatbot model developed by Anthropic, based on the T5 architecture.

Key features of FastChat-T5 include:

  • Strong conversational abilities and optimized for efficient inference
  • Competitive performance on dialogue tasks
  • Released under the Apache 2.0 license

FastChat-T5 is specifically designed for building chatbots and conversational agents. Its compact size and efficient inference make it suitable for real-time chat applications.

16. h2oGPT

Developed by H2O.ai, h2oGPT is a family of open source LLMs ranging from 12B to 20B parameters.

Key features of h2oGPT models include:

  • Prioritizing transparency and strong performance on NLP benchmarks
  • Offering a balance between model size and performance
  • Released under the Apache 2.0 license

h2oGPT models are versatile and can be used for a variety of language understanding and generation tasks. Their focus on transparency makes them suitable for applications that require interpretability and accountability.

17. RedPajama-INCITE

RedPajama-INCITE is a family of open source base, instruction-tuned, and chat models ranging from 3B to 7B parameters.

Key features of RedPajama-INCITE models include:

  • Strong conversational abilities and performance on instruction-following tasks
  • Training on a large corpus of high-quality data
  • Released under the Apache 2.0 license

RedPajama-INCITE models are well-suited for building chatbots, task-oriented dialogue systems, and applications that require following specific instructions. Their strong conversational abilities make them a good choice for engaging and interactive applications.

18. Falcon

Developed by the Technology Innovation Institute (TII) in Abu Dhabi, Falcon is a family of open source LLMs that have made significant strides in 2024. The largest model, Falcon-180B, boasts an impressive 180 billion parameters, making it one of the most powerful open source LLMs available. Falcon models are trained on the RefinedWeb dataset, which consists of high-quality web data, allowing them to outperform models trained on curated corpora.

Key features of Falcon models include:

  • Exceptional performance on a wide range of NLP tasks
  • Efficient inference with optimized architectures
  • Multilingual capabilities, supporting over 100 languages
  • Released under the permissive Apache 2.0 license

Falcon models have found applications in various domains, including content generation, language translation, question answering, and sentiment analysis. Their open source nature and impressive performance have made them a popular choice among researchers and developers.

19. MPT-30B

MosaicML, a leading provider of open source AI models, released MPT-30B in June 2023, setting a new standard for open source foundation models. With 30 billion parameters, MPT-30B demonstrates remarkable capabilities across a wide range of natural language tasks, including text generation, question answering, and summarization.

Notable features of MPT-30B include:

  • State-of-the-art performance on benchmark datasets
  • Efficient training and inference using MosaicML's Composer library
  • Instruction-tuned variants for improved task-specific performance
  • Released under the Apache 2.0 and CC BY-SA-3.0 licenses

MPT-30B has been widely adopted by the AI community, powering applications such as chatbots, content creation tools, and research projects. Its open source nature and strong performance have made it a go-to choice for organizations seeking to leverage the power of large language models.

20. CodeGen

Developed by Salesforce, CodeGen is a series of code-generation models ranging from 350M to 16B parameters.

Key features of CodeGen models include:

  • State-of-the-art performance on code generation tasks like HumanEval
  • Trained on a large corpus of code from multiple programming languages
  • Supports multi-turn conversational program synthesis
  • Released under a non-commercial license

CodeGen models excel at generating code from natural language descriptions. Their multi-turn conversational capabilities enable an interactive development workflow where the model can iteratively refine code based on user feedback. CodeGen is well-suited for AI-assisted programming and code autocompletion.

21. FLAN-T5

FLAN-T5 is a family of instruction-tuned models based on Google's T5 architecture, with sizes ranging up to 11B parameters.

Key features of FLAN-T5 models include:

  • Strong few-shot performance on a wide range of tasks
  • Instruction-tuned on a mixture of over 1800 diverse tasks
  • Outperforms much larger models like PaLM-62B on some benchmarks
  • Released under the Apache 2.0 license

FLAN-T5's instruction-tuning enables it to perform well on unseen tasks with just a few examples. This makes it suitable for applications requiring task-agnostic language understanding and generation capabilities. FLAN-T5 can be used for question answering, summarization, translation, and more.

22. GPT-NeoX-20B-Instruct

GPT-NeoX-20B-Instruct is an instruction-tuned variant of EleutherAI's GPT-NeoX-20B model, demonstrating strong performance on instruction-following tasks.

Key features of GPT-NeoX-20B-Instruct include:

  • Improved ability to follow instructions compared to the base GPT-NeoX-20B
  • Promising results on benchmarks like MMLU and BBH
  • Can be used for applications that require models to follow specific instructions
  • Released under the Apache 2.0 license

The instruction-tuning of GPT-NeoX-20B-Instruct makes it well-suited for building task-oriented systems, such as virtual assistants, that need to understand and execute user instructions. It can also be used for general language tasks where the ability to follow instructions is beneficial.

23. Nous Hermes

Nous Research has developed the Hermes series of open source LLMs, with model sizes ranging from 2.5B to 13B parameters.

Key features of Nous Hermes models include:

  • Competitive performance on language modeling and downstream tasks
  • Efficient implementation using the xFormers library
  • Multilingual variants supporting non-English languages
  • Released under the Apache 2.0 license

Nous Hermes models offer a balance of performance and efficiency, making them suitable for a variety of language understanding and generation tasks. The multilingual variants are valuable for building applications that serve non-English speaking users.

24. Ziya-LLaMA-13B

Ziya-LLaMA-13B is a Chinese LLaMA model with 13B parameters, developed by the Ziya team. It has shown promising performance on Chinese language tasks.

Key features of Ziya-LLaMA-13B include:

  • Strong results on Chinese language modeling and downstream benchmarks
  • Enables building Chinese language applications with state-of-the-art performance
  • Trained on a large corpus of diverse Chinese text data
  • Released under a custom license allowing for flexible usage

Ziya-LLaMA-13B is a valuable resource for researchers and developers working on Chinese NLP applications. It can be used for tasks such as content generation, question answering, and sentiment analysis in the Chinese language.

25. Vicuna

Developed by the Large Model Systems Organization (LMSYS), Vicuna is an open source chatbot model with sizes ranging from 7B to 13B parameters.

Key features of Vicuna models include:

  • Strong conversational abilities and performance on dialogue tasks
  • Fine-tuned on a large corpus of conversational data
  • Released under a non-commercial license

Vicuna models are specifically designed for building engaging and coherent chatbots. Their fine-tuning on conversational data makes them well-suited for applications that require natural and contextually relevant responses.

Conclusion

The open source LLM landscape has seen tremendous growth and progress in 2024, with a wide range of models available for various use cases and deployment scenarios. From large-scale models like Falcon-180B and MPT-30B to more specialized models like FastChat-T5 and Vicuna, there are open source LLMs suitable for a variety of applications.

As the field continues to evolve, we can expect further advancements in model architectures, training techniques, and downstream task performance. The open source nature of these models will continue to drive innovation, collaboration, and accessibility in the AI community.

When selecting an open source LLM for a specific use case, it's important to consider factors such as model size, context length, training data, licensing terms, and performance on relevant benchmarks. The models discussed in this article provide a starting point for exploring the capabilities and potential of open source LLMs in 2024.

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