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10 open-source LLMs to know in 2024

Let’s take a brief look at what open-source LLMs are, their benefits, the risks involved, and open-source LLMs you should get to know in 2024. 

Open Source LLMs
Or Jacobi Or Jacobi
8 min read Apr 11, 2024

Last year’s ChatGPT and Midjourney explosion, sparked a race for everyone to develop their own open source LLMs. From Hugging Face’s LLM leaderboard to what seems like a new model announced weekly, there’s more than enough proof that this surpassed the “trending” tag. Everyone wants to build the best. 

Businesses know what LLMs can offer – new revenue streams and organizational efficiency like never before. From SaaS companies to industry leaders, every engineering and product team is focused on building new AI apps and features. 

But before that, let’s take a brief look at what open-source LLMs are, their benefits, the risks involved, and open-source LLMs you should get to know in 2024. 

What are Open Source LLMs?

Large Language Models (LLMs) are built on the standard of deep learning, a field of AI that mimics the human brain. Now, as for their types, they are divided into two. On one side, we have the open-source LLMs. These models are the open books of the AI world. They lay out all of their information for the general public. It includes training datasets, the architecture of the models, and the weights.

Alternatively, the proprietary or closed-source models like the famous GPT-3, keep their information closed and hidden. Nobody knows how they’re trained and what data they are using. This information is only accessible to their creators.

Benefits of open source LLMs in enterprise

Let’s take a look at the benefits of open-source LLMs.

  • Enhanced Data Security and Privacy: These LLMs enable organizations to run models on their infrastructure with ease. Also, they allow them complete control over data security and privacy.
  • Cost Savings: Open-source LLMs are a cost-effective solution for large businesses as well as startups. And they do this by simply eliminating the licensing fees.
  • Code Transparency: The open nature of AI models ensures transparency. It allows for in-depth inspection and validation. This fosters trust and adherence to industry standards.
  • Ethical and Responsible AI: The open-source ethos often emphasizes ethical AI development. This leads to the creation of more fair and unbiased models. 
  • Interoperability: These open-source models are designed with compatibility in mind. It ensures smooth integration with diverse systems and tools, augmenting their utility in complex IT landscapes.

Risks associated with LLMs

Just like with software, LLMs also have their bugs. Remember, not all LLM outputs should be trusted. Every technology has its drawbacks. We just need to weigh them and see if the risks are worth it or not. Let’s take a look at the risks that come with LLMs.

Hallucinations

LLMs can sometimes generate information that sounds convincing but is entirely false. This phenomenon is known as “AI Hallucinations”. These hallucinations can range from amusing to life-threatening. It is an inherent issue with LLMs that starts with the foundational models. While there are numerous claims that RAG (retrieval-augmented generation) is a cure for hallucinations, that is far from true. 

Bias

Bias in LLMs happens when the data they’re trained on isn’t diverse or doesn’t represent real-world examples. This skewed perspective can lead the LLM to generate outputs that perpetuate stereotypes or unfair assumptions.

Privacy and ethics 

With the usage of AI, ethical considerations are almost the first things that come to notice. It raises questions about whether the collected data respects privacy and complies with regulations. These models should also provide proper information to the individuals whose data is being used.

Data leakage

These security issues range from the leakage of personally identifiable information (PII) to sensitive and proprietary organizational data, like source code or financial records. 

Prompt injection

Malicious users could “jailbreak” an LLMs prompt and take control. This trick opens AI apps up to hostile injections that damage user trust and can lead to bad PR.

How to mitigate risks in open-source LLMs

Generative AI requires real-time guardrails to keep its actions safe and within ethical limits. These safeguards help organizations control AI behavior, ensuring it always follows current rules and norms. In the end, an LLM-powered app is not effective if its not meeting its KPIs, and living up to its intended goals. 

Implementing AI guardrails is the proactive solution to safeguarding users and ensuring AI integrity and reliability. Solutions like Aporia Guardrails offer an enterprise-wide solution to mitigating hallucinations and off-topic content, and preventing data leakage, prompt injections, and other risks in real time. This not only boosts confidence in your chatbot’s performance but also frees engineering, product, and security teams to focus on more urgent tasks. 

Why guardrails:

  • Ensure users can trust your AI’s output.
  • Protect brand reputation from embarrassing AI hallucinations.
  • Keep your AI app on target, don’t let it go off-topic.
  • Easy to integrate guardrails that fit any use case and every LLM. 

10 Open Source LLMs

We’ve talked a bit about what LLMs are, their risks, and how they can benefit businesses. Now, let’s explore 10 open-source LLMs that we have listed below after careful consideration. These are not ranked in any order.

1. GPT-NeoX

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GPT-NeoX, developed by EleutherAI, stands as a beacon of open-source innovation in the LLM arena, mirroring GPT-3’s architecture with a robust 20 billion parameters. This model is tailored for tasks requiring few-shot reasoning, such as code generation and content writing. However, harnessing its full potential demands significant computational resources. 

2. BLOOM

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BLOOM emerges as a titan in the open-source LLM landscape, a masterpiece created by BigScience. BLOOM is not just any language model; it’s a polyglot genius capable of generating text across 46 natural and 13 programming languages. This feat crowns BLOOM as the world’s largest open multilingual language model.

3. LLaMA 2

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This model is developed by the innovators at Meta AI. This next-gen collection spans an impressive range, from 7 billion to a whopping 70 billion parameters, showcasing a variety of pre-trained and fine-tuned marvels. What truly sets LLaMA 2 apart is its superior performance across numerous external benchmarks, outshining its peers in areas critical for AI excellence, such as reasoning, coding, language proficiency, and knowledge retention.

4. BERT

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Google’s BERT is an open-source LLM that has set a new standard in natural language processing (NLP). BERT’s success is based on its unique bidirectional training, which allows the model to absorb context from both sides of a text segment, a feature not available in traditional models. It’s designed to play well with popular frameworks like TensorFlow and PyTorch, making it a versatile tool for developers and researchers alike.

5. XGen-7B

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Salesforce’s XGen-7B is a game-changing contribution to the expansive universe of LLMs, setting a new benchmark with its 7 billion parameters. Its prowess stems from its diverse training on a wide range of datasets, including those focused on instructional content, granting it a refined understanding of directives.

6. Falcon-180B

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The Technology Innovation Institute (TII) has unleashed Falcon-180B with its 180 billion parameters, overshadowing many of its peers in both scale and capability. This model stands out as a causal decoder-only powerhouse, adept at weaving coherent and contextually relevant narratives across a multitude of languages including, English, German, Spanish, and French.

7. Vicuna-33B

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Large Model Systems (LMSys) has unveiled Vicuna-33B, an LLM that is pushing the boundaries of NLP. This model, with its 33 billion parameters, stands out due to its unique training approach. It was fine-tuned using conversations from ShareGPT.com, making it highly adept at understanding and generating human-like text.

8. Dolly 2.0

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Dolly 2.0 is skillfully crafted by the experts at Databricks. This model is positioned as a compelling alternative to commercial giants like ChatGPT, boasting a robust 12-billion parameter setup. What sets Dolly 2.0 apart is its unique training dataset, databricks-dolly-15k, a collection of 15,000 prompt and response pairs meticulously created by Databricks employees.

9. CodeGen

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CodeGen stands out as Salesforce AI Research’s innovative contribution to the field of AI-driven programming, built on the solid GPT-3.5 architecture. This model is versatile and available in various sizes to cater to different computational and application needs. Its key features include an exceptional ability for code generation, and translating simple English instructions into functional code across various languages.

10. Platypus 2

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Platypus 2 is developed by Cole Hunter and Ariel Lee. This model is a product of extensive training on the Open-Platypus dataset, which integrates the insights of thousands of LLMs. Key features that set Platypus 2 apart include advanced data protection to prevent leaks, efforts to eliminate biases for fairer outputs, strategies to reduce data redundancy for more effective learning, and a combination of speed and cost-efficiency in training and adaptation.

Conclusion

Open-source LLMs have opened new horizons for businesses, researchers, and developers alike. They tend to offer more flexibility and customization opportunities, allowing product teams to adapt the model to their needs, often with lower upfront costs.

However, with the benefits of LLMs come the risks. Hallucinations can keep your AI app from reaching production. Making it important to prioritize your AI app’s safety and reliability when interacting with users. 

Set your AI apps up for success and mitigate risks with Aporia Guardrails.

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