The revolution in generative AI is driven by open-source large language models (LLMs) that enhance accessibility, transparency, and innovation. These 30 leading open-source LLMs of 2024, highlight their strengths, cost-efficiency, and suitability for various tasks. How can you identify models that offer the best performance and cost-efficiency?

The revolution in generative AI is driven by large language models (LLMs), which use advanced neural architectures to understand and generate human language. These models are the foundation of popular chatbots like ChatGPT and Google Bard. However, many of these models are proprietary, limiting their accessibility and transparency. Fortunately, the open-source community has responded by developing LLMs that promise to enhance accessibility, transparency, and innovation in AI.

Large language models are designed to handle a variety of tasks, from natural language processing to programming assistance. Given the rapid advancements in this field, it's essential to stay updated with the latest developments. 

30 Open-Source Large Language Models For 2024

1. Mistral 7B

Mistral 7B is an open-source LLM developed by Mistral AI, showcasing promising performance and supporting extended context lengths. Its capabilities make it particularly well-suited for tasks involving long texts, such as document summarisation, long-form question answering, and context-aware generation. The model utilises sliding window attention, which allows for efficient processing of very long input sequences.

Key features of Mistral 7B include:

  • Competitive performance on language modelling and downstream tasks
  • Long context length of 4,096-16,000 tokens using sliding window attention
  • Released under the Apache 2.0 licence

2. GPT-NeoX-20B

GPT-NeoX-20B, developed by EleutherAI, is an open-source autoregressive language model with 20 billion parameters. It excels in generative tasks such as story writing, article generation, and creative writing. Its robust language modelling capabilities make it a preferred choice for applications requiring coherent and sophisticated text generation.

Key features of GPT-NeoX-20B include:

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

3. GPT-J-6B

GPT-J-6B is a 6 billion parameter open-source language model developed by EleutherAI. This versatile model is suitable for a wide range of language generation and understanding tasks. Its moderate size makes it more accessible for deployment compared to larger models.

Key features of GPT-J-6B include:

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

4. BLOOM

BLOOM, developed by BigScience, is a 176-billion-parameter open-access multilingual language model that has gained significant adoption since its release in 2022. Its multilingual capabilities and strong performance make it a compelling choice for applications serving diverse linguistic audiences. It is particularly well-suited for tasks like translation, multilingual content generation, and cross-lingual understanding.

Key features of BLOOM include:

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

5. T5 (Text-to-Text Transfer Transformer)

Developed by Google Research, T5 has become a cornerstone in the open-source AI community. Its unified text-to-text framework simplifies various NLP tasks, from translation to summarisation.

Key features of T5 include:

  • Unified text-to-text framework enabling a wide range of NLP tasks
  • Strong performance on tasks like translation, summarisation, and question-answering
  • Extensive pretraining on diverse datasets, enhancing generalisation across tasks

6. OPT-175B

The year 2022 saw the release of Open Pre-trained Transformers (OPT), a significant step towards Meta's mission to democratise the LLM industry through open-source technology. OPT is a set of decoder-only pre-trained transformers ranging from 125 million to 175 billion parameters. OPT-175B is one of the most advanced open-source LLMs available today, comparable in performance to GPT-3. Both the source code and pre-trained models are accessible to the public.

Key features of OPT-175B include:

  • State-of-the-art performance comparable to GPT-3
  • Wide range of parameter sizes, offering flexibility for various applications
  • Released under a permissive open-source licence

7. XGen-7B

Many companies are entering the LLM race, and Salesforce joined with its XGen-7B LLM in July 2023. Unlike most open-source LLMs, which provide limited information with short prompts, XGen-7B aims to support longer context windows. The most advanced version of XGen, XGen-7B-8K-base, allows for an 8K context window, accommodating the cumulative size of the input and output text. Efficiency is also a top priority in XGen, which uses only 7 billion parameters for training, much less than other powerful open-source LLMs like LLaMA 2 or Falcon. Despite its smaller size, XGen can still deliver excellent results. The model is available for commercial and research purposes, except for the XGen-7B-{4K,8K}-inst variant, which is trained on instructional data and RLHF and is released under a noncommercial licence.

Key features of XGen-7B include:

  • Supports long context windows up to 8,000 tokens
  • Efficient training with only 7 billion parameters
  • Available for both commercial and research purposes

8. Pythia

Pythia is a suite of open-source LLMs ranging from 70 million to 12 billion parameters, aimed at enabling analysis of language models across training and scaling. 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 specific downstream tasks.

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 licence

9. OLMo

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

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 licence

10. Gemma

Gemma is a family of open-source LLMs developed by Google, with unique features like support for long-range context up to 8,192 tokens. Gemma's long context length makes it particularly well-suited for tasks involving extended text, such as document summarisation, long-form question answering, and content generation. Its multilingual variants are valuable for various applications.

Key features of Gemma models include:

  • Competitive performance on language modelling 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

11. Dolly

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

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 licence

12. StableLM-Alpha

StableLM-Alpha is a suite of open-source LLMs ranging from 3 billion to 65 billion parameters, developed by Stability AI. StableLM-Alpha's long context length makes it suitable for tasks involving longer input sequences, such as document understanding, summarisation, and context-aware generation. The range of model sizes allows for flexibility in deployment.

Key features of StableLM-Alpha models include:

  • Promising performance on language modelling and downstream tasks
  • Long context length of 4,096 tokens, enabling a better understanding of extended text
  • Released under the CC BY-SA-4.0 licence

13. RWKV

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

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 modelling and downstream tasks
  • Released under the Apache 2.0 licence

14. FastChat-T5

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

Key features of FastChat-T5 include:

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

15. h2oGPT

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

Key features of h2oGPT models include:

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

16. RedPajama-INCITE

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

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 licence

17. 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. 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.

Key features of Falcon models include:

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

18. Vicuna 13-B

Vicuna 13-B is an open-source language model developed by the team at Vicuna AI, featuring 13 billion parameters. It is designed to provide high-quality performance in various natural language processing tasks, with a focus on generative capabilities and contextual understanding. Vicuna 13-B is particularly well-suited for applications requiring nuanced language generation and detailed responses.

Key features of Vicuna 13-B include:

  • High-quality generative capabilities across a range of NLP tasks
  • Strong contextual understanding, making it ideal for complex text generation
  • Released under the open-source Apache 2.0 licence

19. LLaMA 2

LLaMA 2, developed by Meta AI, is a suite of large language models ranging from 7 billion to 70 billion parameters. It builds on the success of the original LLaMA models with improved performance and efficiency. LLaMA 2 is widely used for tasks such as text generation, translation, and summarisation, benefiting from its advanced training techniques and optimised architecture.

Key features of LLaMA 2 include:

  • Improved performance and efficiency compared to its predecessor
  • Versatile application across a range of NLP tasks
  • Released under a permissive open-source licence, promoting wide adoption

20. BERT

BERT (Bidirectional Encoder Representations from Transformers), developed by Google AI, revolutionised the field of NLP with its bidirectional approach to language modelling. BERT is designed to understand the context of words in a sentence by considering both their left and right contexts. It has become a foundational model for various NLP tasks, including question-answering and language inference.

Key features of BERT include:

  • Bidirectional context understanding, enhancing language comprehension
  • Strong performance on tasks like question answering and sentence classification
  • Released under the Apache 2.0 licence, supporting broad use and modification

21. Baichuan-13B

Baichuan-13B is an open-source LLM developed by Baichuan AI, featuring 13 billion parameters. It is designed for a variety of natural language understanding and generation tasks, offering strong performance in text generation and comprehension. Baichuan-13B stands out for its efficiency and adaptability, making it suitable for both research and practical applications.

Key features of Baichuan-13B include:

  • Versatile performance in natural language understanding and generation
  • Efficient and adaptable for various applications
  • Released under the open-source Apache 2.0 licence

22. CodeGen

CodeGen is an open-source language model developed for code generation and programming tasks. With a focus on generating code snippets and providing programming assistance, CodeGen supports multiple programming languages and offers high-quality code completion and generation capabilities.

Key features of CodeGen include:

  • Specialised in code generation and programming tasks
  • Supports multiple programming languages for diverse use cases
  • Released under the open-source Apache 2.0 licence

23. MPT-30B

MPT-30B, developed by MosaicML, is an advanced open-source language model featuring 30 billion parameters. It is designed for high-performance language modelling, including text generation, comprehension, and summarisation. MPT-30B excels in handling complex and large-scale NLP tasks, making it a valuable tool for both research and practical applications.

Key features of MPT-30B include:

  • High-performance capabilities for text generation and comprehension
  • Effective handling of complex and large-scale NLP tasks
  • Released under the open-source Apache 2.0 licence

24. FLAN-T5

FLAN-T5 is a family of instruction-tuned models based on Google's T5 architecture, with configurations ranging up to 11 billion parameters. FLAN-T5 models are specifically tuned to perform effectively on tasks they have not seen before, with minimal examples. This instruction tuning makes FLAN-T5 particularly versatile for various applications, including question-answering, summarisation, and translation.

Key features of FLAN-T5 models include:

  • Strong performance with few-shot learning on a wide range of tasks
  • Instruction-tuned on a diverse set of over 1800 tasks
  • Outperforms larger models like PaLM-62B on certain benchmarks
  • Released under the Apache 2.0 licence

25. GPT-NeoX-20B-Instruct

GPT-NeoX-20B-Instruct is a variant of EleutherAI's GPT-NeoX-20B, specially tuned for following instructions. This model excels in tasks that require precise execution of user directives, making it ideal for virtual assistants and similar applications. It also shows promising performance in general language tasks where instruction adherence is beneficial.

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

  • Enhanced instruction-following capabilities compared to the base GPT-NeoX-20B
  • Strong results on benchmarks like MMLU and BBH
  • Suitable for applications needing robust instruction adherence
  • Released under the Apache 2.0 licence

26. Nous Hermes

Developed by Nous Research, the Hermes series features open-source LLMs with sizes ranging from 2.5 billion to 13 billion parameters. These models balance performance with efficiency, offering robust capabilities for both language understanding and generation. The multilingual variants are particularly useful for applications aimed at non-English speaking audiences.

Key features of Nous Hermes models include:

  • Competitive performance in language modelling and various NLP tasks
  • Efficient implementation leveraging the xFormers library
  • Multilingual support for non-English languages
  • Released under the Apache 2.0 licence

27. Ziya-LLaMA-13B

Ziya-LLaMA-13B is a Chinese-language variant of the LLaMA model, developed by the Ziya team, with 13 billion parameters. It is designed to excel in Chinese NLP tasks, providing strong performance in content generation, question answering, and sentiment analysis. Ziya-LLaMA-13B is a valuable asset for applications focusing on Chinese language processing.

Key features of Ziya-LLaMA-13B include:

  • Excellent performance on the Chinese language benchmarks
  • Capable of advanced Chinese language applications
  • Trained on a comprehensive corpus of Chinese text data
  • Released under a custom licence for flexible usage

28. Stable Beluga 2

Stable Beluga 2 is an advanced open-source language model developed by Stability AI. It builds upon the success of its predecessor, featuring enhanced capabilities for both language understanding and generation. Stable Beluga 2 is designed to handle diverse NLP tasks efficiently, with a focus on long-context handling and high performance.

Key features of Stable Beluga 2 include:

  • Superior performance on various language modelling and generation tasks
  • Efficient handling of long input sequences for improved context understanding
  • Released under the open-source Apache 2.0 licence

29. ProphetNet

ProphetNet, developed by Microsoft Research, is an open-source model designed for sequence-to-sequence tasks such as text generation and summarisation. It is notable for its innovative approach to predicting future tokens based on past sequences, enhancing its performance on tasks requiring deep contextual understanding and generation.

Key features of ProphetNet include:

  • Advanced sequence-to-sequence prediction capabilities
  • Effective for tasks like summarisation and text generation
  • Released under the open-source Apache 2.0 licence

30. DialoGPT

DialoGPT, developed by Microsoft, is an open-source conversational model fine-tuned from the GPT-2 architecture specifically for dialogue generation. It is designed to produce coherent and contextually relevant responses in conversational settings, making it a strong candidate for building chatbots and virtual assistants.

Key features of DialoGPT include:

  • Specialised in generating engaging and contextually appropriate dialogue
  • Fine-tuned conversational datasets for improved interaction quality
  • Released under the open-source MIT licence

Choosing the Right Open-Source LLM for Your Needs

The field of open-source Large Language Models (LLMs) is rapidly evolving, with an increasing number of options becoming available. As developers across the globe collaborate to enhance these models, the performance gap between open-source and proprietary LLMs is narrowing. Given this dynamic landscape, selecting the right open-source LLM for your specific needs can be challenging. Here are some key factors to consider:

Define Your Goal: Understanding your primary goal for using an LLM is crucial. While many open-source LLMs are accessible, some are tailored specifically for research purposes rather than practical applications. Clarify whether you need an LLM for experimental work or if you require one for production-level tasks.

Assess the Necessity: Evaluate whether an LLM is essential for your project. If your objectives can be achieved without an LLM, opting for alternative solutions might save you both time and resources. Determine if the benefits of using an LLM outweigh the potential drawbacks for your particular use case.

Consider Accuracy: Accuracy is often a significant consideration when choosing an LLM. Generally, larger models tend to offer higher accuracy due to their extensive training data and more complex architectures. Assess how critical accuracy is for your project and choose an LLM that meets these requirements.

Budget Constraints: Consider your budgetary constraints when selecting an LLM. Larger models, while potentially more accurate, require substantial computational resources for both training and deployment, which can be costly. Evaluate the total cost involved and ensure it aligns with your budget.

Evaluate Pre-trained Options: Explore whether a pre-existing, pre-trained LLM could fulfil your needs. Many open-source LLMs come pre-trained for specific tasks or domains, which can save you from the effort and expense of developing and training a model from scratch. Assess available pre-trained models to see if they meet your requirements effectively.