Open Source LLM in 2024: Your Comprehensive Guide for Open-Source Large Language Models

The past decade has seen a surge in the emergence of cutting-edge smart technologies, the most notable being the creation and subsequent mainstream infiltration of open-source large language models (LLMs).

Large language models present an easy way to search, analyze, summarize, and generate content through natural language processing. However, every large language model comes with unique features and capabilities, thus necessitating the need to choose the right one for your specific use case and application specifications.

In that regard, we’ve compiled a comprehensive LLM guide detailing the benefits of using a LLM and some of the best open-source large language models on the market.

Open Source LLM Explained: Understanding Large Language Models

Large language models, popularly abbreviated as LLMs are a type of artificial intelligence (AI) algorithms that use deep learning techniques and massive datasets of training data to understand, generate, and summarize content. [1]

This makes them suitable for a wide variety of natural language processing tasks, including sentiment analysis, text-to-speech synthesis, text summarization, token summarization, and much more.

Unlike their proprietary counterparts, which are owned and operated by the parent company and can only be used after purchasing a license, open-source large language models are free and allow anyone to access, modify, distribute, and use them for any purpose.

The mere fact that their underlying infrastructure is available to the public makes them more versatile as developer communities constantly upgrade and optimize them for specific use cases.

Top Open Source LLMs: Leading Models of 2024

Choosing a reliable open-source LLM isn’t just about picking one with the most parameters – it’s about choosing an LLM that’s perfectly suited to your needs and gives you the chance to optimize it even further. [2]

In that regard, here are some of the best open-source LLMs available:

XLNet

XLNet is a product of collaborative efforts by Google and researchers at Carnegie Mellon University. The model comes with a unique autoregressive formulation that enables it to learn bidirectional contexts by maximizing the likelihood over all permutations of the factorization order, thereby following a generalized autoregressive pre-training method. [3]

Essentially, the model offers the best of autoencoding and autoregressive language modeling, while avoiding the limitations of both. Unlike most autoregressive models, XLNet amplifies the expected likelihood of a sequence with respect to all possible permutations of the factorization order.

Thanks to this unique operation, the context of each position can consist of tokens from both the right and left. In anticipation, the model learns to utilize the contextual information of any content from all positions.

XLNet also integrates the relative encoding scheme and segment recurrent mechanism of Transformer-XL into pre-training, thus improving its performance for tasks involving longer text sequences.

LLaMA

Developed by Meta AI, LLaMA is a collection of several open-source large language models that vary in size ranging from 7 to 65 billion parameters. The open-source LLM is available in various sizes, including 6.7 billion, 13.0 billion, 32.5 billion, and 65.2 billion parameters. Each model excels in different tasks, with the larger models performing better on more complex tasks.

LLaMA is based on the transformer architecture, which has been the standard model architecture for most LLMs since 2018. The open source LLM shares several similarities with GPT-3 but also comes with a few differences. For instance, instead of the ReLU activation features used in GPT-3, LLaMA uses SwiGLU activation functions.

One of the most alluring aspects of the LLaMA models is they’re trained in diverse domains. This makes them suitable for various applications, including answering questions, translating, summarizing text, and more. They can also be fine-tuned for various AI projects.

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LLaMA 2

This is the second iteration of the LLaMA model. LLaMA 2 is specifically designed for dialogue applications. As such, the model has undergone extensive fine-tuning to make it comparable to other dialogue models like ChatGPT. Like its predecessor, LLaMA, LLaMA2 comes in three sizes: 7, 13, and 70 billion parameters. [4]

That said, LLaMA 2 has significant improvements and advancements over its predecessor. For starters, LLaMA 2 is trained on a new set of publicly available data, with a pre-training corpus that’s 40% larger than LLaMA. The context length of LLaMA 2 is also double that of LLaMA, and it utilizes a grouped query attention mechanism, giving it better performance over its predecessor.

LLaMA 2 also has another iteration called LLaMA 2-Chat that is fine-tuned and optimized for chat-based interactions. These further developments ensure that the model’s output is safe and helpful, thus outperforming most mainstream models, including ChatGPT, on human evaluation benchmarks.

The open-source large language model is licensed for researchers and commercial entities, allowing a wide range of users to leverage the model’s capabilities for various purposes, including research, specialized projects, and commercial applications.

What’s even more impressive is the wide availability of training data available for the model. Developers can utilize tokens from publicly available sources and access fine-tuning data from over one million new human-annotated examples and publicly available instruction datasets.

Guanaco

Guanaco is part of a family of large language models based on Meta’s LLaMA models. It is built on the foundation of the LLaMA B model and fine-tuned using the innovative QLoRA (Quantized Low-Rank Adapters) method. QLoRA allows the model to be fine-tuned on a single GPU, making it more accessible to small organizations and private developers.

The QLoRA fine-tuning method also quantifies the model to 4-bit precision and incorporates LoRAS (low-rank adaptive weights), significantly reducing memory requirements and optimizing its performance. This approach allows even the largest 65 billion-parameter Guanaco open-source LLM to function effectively with less than 48 GB of GPU memory, down from 780 GB of GPU memory without compromising performance.

One of Guanaco’s most impressive and distinctive features is its adaptability to extended conversations, with its capability to answer questions or discuss topics upon request, making it especially suitable for chatbot applications.

On the downside, despite its robust performance in natural language processing tasks, the open-source LLM is not licensed for commercial applications. It is primarily intended for academic research and non-commercial applications.

Read more about LLM implementation strategy: Preparation guide for using LLMs

Alpaca

Alpaca is a large language model developed by researchers at Stanford University’s Center for Research on Foundation Models (CRFM). The model was fine-tuned from Meta AI’s LLaMA 7B model and 52,000 instruction-following demonstrations in the self-instruction style. [5]

This gives Alpaca robust instruction-following capabilities, making it a reliable option for natural language processing tasks that require strict adherence to instructions. This LLM is primarily intended for academic research, but it’s not yet ready for general use due to inadequate safety measures.

It is also not available for commercial use because its instruction data is based on OpenAI’s text-davinci-003, whose terms and conditions prohibit the development of models that compete with OpenAI.

Besides data from OpenAI’s text-davinci-003, the developers also used Hugging Face’s training framework to fine-tune Alpaca, taking advantage of mixed precision training and Fully Sharded Data Parallel.

RedPajama

RedPajama is a product of collaborative efforts between ETH DS3Lab, Hazy Research, Ontocord.ai, and Together, who embarked on the project with a mission to create a set of leading, fully open-source large language models.

The primary objective of the project is to bridge the quality gap between proprietary and open-source LLMs since most powerful foundational models are currently locked behind commercial APIs, which limit customization, research, and usage with sensitive data.

The RedPajama project consists of three major components including:

• RedPajama Dataset   

This is an impressive 1.2 trillion token open-source dataset created following the recipe described in the LLaMA paper. This impressive dataset comprises 7 data slices from diverse sources, including C4, GitHub, CommonCrawl, Books, StackExchange, Wikipedia, and arXiv.

Each data slide undergoes meticulous filtering and pre-processing to ensure data quality and token count alignment with the numbers reported by Meta AI in the LLaMA paper. [6]

• RedPajama Base Models

RedPajama is made up of two base models: 3 billion and 7 billion parameter models, both developed based on the Pythia architecture. The models also have other variations, including the RedPajama-INCITE-Instruct-3B-v1 and RedPajama-INCITE-Chat-3B-v1.

The RedPajama-INCITE-Chat-3B-v1 model is optimized for conversational AI tasks and is capable of generating human-like content in a conversational context. The RedPajama-INCITE-Instruct-3B-v1, on the other hand, is designed to follow instructions effectively, making it perfectly suited for tasks that require the execution of complex instructions.

• RedPajama Instruction Tuning Data and Models

The final component of the triage focuses primarily on fine-tuning the models to excel in specific tasks. It offers variations of the RedPajama-INCITE-Base models, with each variation coming with distinct characteristics and applications.

For instance, the RedPajama-INCITE-Chat models are fine-tuned using Open Assistant and Dolly 2.0 data. Conversely, the RedPajama-INCITE-Instruct models are designed for few-shot prompts, which helps eliminate any dataset that overlaps with the HELM benchmark. [7]

Stable Beluga

A product of CarperAI and Stability AI, the Stable Beluga project consists of two models: Stable Beluga 1 and Stable Beluga 2. Both models were built upon Meta AI’s LLaMA open-source LLM models and fine-tuned using new synthetically generated datasets in the standard Alpaca format. [8]

The primary purpose of the project was to bridge the quality gap between proprietary (closed) and open-source LLMs. Stable Beluga 1 and Stable Beluga 2 utilize the LLaMA 65B and LLaMA 2 70B foundation models, respectively, allowing them to perform fairly well across various benchmarks. In fact, Stable Beluga 2 has outperformed LLaMA 2 in certain benchmarks.

One of the most alluring attributes of the Stable Beluga models is their capability to solve complex problems in specialized fields such as mathematics and law, with a strong focus on fine linguistic details. Since they were released, the models have shown proficiency in responding to complex questions and handling reasoning tasks, making them especially suitable instruments for researchers in specific domains.

Since it’s categorized as a research instrument, Stable Beluga comes under a non-commercial license, further emphasizing the developers’ commitment to promoting research and accessibility in the AI community. The non-commercial license ensures that the models are freely available for academic and other non-commercial purposes, thus encouraging innovation and collaboration in natural language processing.

The Stable Beluga models’ training process is based on the Orca approach, which is quite similar to Microsoft’s progressive learning methodology. That said, the datasets used in the Stable Beluga project aren’t similar to the Orca paper. Instead, the development team utilized Enrico Shippole’s datasets, which included NIV2 Submix Original, COT Submix Original, T0 Submix Original, and FLAN 2021 Submix Original to prompt large language models.

The resulting dataset contained 600,000 high-quality examples, accounting for about 10% of the original Orca data set. Finally, the developers filtered the dataset and removed evaluation benchmarks, resulting in perfectly fine-tuned models with exceptional performance.

MPT

Developed by MosaicML, MPT models are a series of transformer-based language models designed for commercial use. However, unlike most models designed for commercial use, MPT models are built on the GPT-3 model, giving them greater efficiency and flexibility in various natural language processing tasks.

MPT models come in a number of variations, with MPT-7B-StoryWriter and MPT-7B serving as the two most superior models. The former, MPT-7B Base, is a decoder-only transformer model with 6.7 billion parameters and trained on a corpus of one million tokens of code and text curated by MosaicML’s data team. [9] The base model utilizes AliBi for handling long context lengths and FlashAttention for shorter context lengths.

The model has an Apache 2.0 license but, unfortunately, is not intended for deployment without fine-tuning. Developers also recommend guardrails and user consent for human-facing interactions.

The MPT-7B-StoryWriter-65k+, on the other hand, is a variant of MPT-77 B perfectly tailored for reading and writing stories with incredibly long context lengths. The model is a result of fine-tuning MPT-7B on a filtered fiction subset of the books3 dataset, which features 65,000 tokens, enabling the model to generate content as long as 84,000 tokens on a single node of A100-80GB GPUs.

Besides the two variations mentioned above, the MPT model series also includes MPT-7B-Chat and MPT-7B-Instruct, which are fine-tuned for specific purposes. The MPT-7B-Chat is designed to serve as a chatbot-style model for dialogue generation; the model is fine-tuned on multiple datasets, including Alpaca, HC3, Helpful, Harmless, Evol-Instruct, and ShareGPT-Vicuna datasets. However, its CC-By-NC-SA-4.0 license limits its usage to non-commercial purposes.

Conversely, the MPT-7B-Instruct model is tailored for short-form instruction following. The model was created by fine-tuning MPT-7B on a dataset released by MosaicML. The dataset was derived from Databricks Dolly-15k, Harmless, and Anthropic’s Helpful datasets.

MPt-7B’s training process used 8 A100-80GB GPUs with a LION optimizer, shared data parallelism, and the Fully Sharded Data Parallelism (FSDP) technique. Developers also employed a gradient checkpoint to optimize memory usage during training. The model has 6.7 billion parameters, a vocabulary of 50432 words with a sequence length of 65536, 16 attention heads, and 32 transformer layers, each with a hidden size of 4096.

It might be interesting for you: LLM Document Analysis: Extracting Insights from Unstructured Data

What are the benefits of open-source LLMs?

Despite the benefits and potential business applications of proprietary large language models, they come at a steep cost and require extensive computation power to utilize effectively. Fortunately, open-source large language models help overcome the cost constraints of proprietary models. Here are a few other benefits of utilizing LLMs:

Transparency and flexibility

One of the greatest hindrances to most proprietary LLMs is the limited power organizations have over the safety of their data. Platforms like GPT-3 cannot properly secure private and corporate data since it’s constantly evaluated by OpenAI’s teams.

Open Source LLMs provide unmatched transparency and flexibility by allowing organizations, even those without a dedicated machine learning team, to deploy the models within the organization’s infrastructure. This gives them full control over their data and ensures that all sensitive information stays within the organization’s network, thus reducing the risk of unauthorized access and data leaks.

Also, unlike closed, proprietary LLMs, Open source LLMs offer transparency regarding how they work, their architecture, training data, methodologies, and how to utilize them effectively.

The ability to inspect code and unmitigated visibility into the algorithm allows the models to gain more trust among organizations, helps ensure ethical and legal compliance, and assists in audits. Optimizing an open-source large language model effectively can also reduce latency and improve performance.

Cost savings

Open-source large language models are considerably cheaper than their proprietary counterparts since they don’t require any licensing fees. That said, any organization looking to utilize LLMs must be prepared to shoulder the initial roll-out costs as well as cloud or on-premises computational infrastructure.

Active community support

The open-source large language model movement aims to democratize the use and access of LLM and generative AI technologies. One of the best ways to do this is by allowing developers to inspect the inner workings of LLMs to propel future development.

By lowering entry barriers to developers and coders around the world, LLMs can foster innovation and improve existing models by increasing accuracy, reducing biases, and increasing overall performance.

Wrapping up

Large language models are poised to change the way institutions conduct research, analyze, and generate content. The development of LLMs will further propel the widespread adaptation and subsequent improvement of LLMs by making them more easily available.

Considering the fact that the technology is only a few years old and can already generate human-like content, we expect to see further developments in the technology, including multimodal capabilities whereby models will be able to understand and generate diverse data types, including text, videos, and images from a unified platform.

References

[1] Towardsdatascience.com. Introduction to Open Source LLMs. URL: https://towardsdatascience.com/a-gentle-introduction-to-open-source-large-language-models-3643f5ca774. Accessed on Monday 20, 2023
[2] Aim research.co. Choosing the Right Open Source LLM: Factors to Consider. URL: https://aimresearch.co/2023/06/13/choosing-the-right-open-source-llm-factors-to-consider/, Accessed on November 20, 2023
[3] Arxiv.org. SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling With Backtracking. URL: https://arxiv.org/abs/2306.05426, Accessed on November 20, 2023
[4] AI.meta. LLMA. URL: https://ai.meta.com/llama/, Accessed on November 20, 2023
[5] Crfm.stanford.edu. Alpaca. URL: https://crfm.stanford.edu/2023/03/13/alpaca.html. Accessed on November 20, 2023
[6] Arxiv. org. LLaMA: Open and Efficient Foundation Language Models. URL: https://arxiv.org/abs/2302.13971. Accessed on November 20, 2023
[7] Deepleaps.com. Open Source Community Releases Redpajama INCITE AI Models, Surpassing Leading Benchmarks. URL: https://deepleaps.com/news/open-source-community-releases-redpajama-incite-ai-models-surpassing-leading-benchmarks/, Accessed on November 20, 2023
[8] Opendatascience.com. Stability AI Has Released Stable Beluga 1 and Stable Beluga 2 New Open-Access LLMs. URL: https://opendatascience.com/stability-ai-has-released-beluga-1-and-stable-beluga-2-new-open-access-llms/. Accessed on November 21, 2023
[9] Kdnuggets.com. Introducing MPT7B, a New Opensource LLM. URL: https://www.kdnuggets.com/2023/05/introducing-mpt7b-new-opensource-llm.html. Accessed on November 21, 2023