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Top 10 Open-Source LLMs models for commercial use

The advancement of large language models (LLMs) is a major development in the technology field. These tools not only revolutionise interaction with machines but also present unique opportunities for creativity and innovation. In this blog post, we will explore open-source LLMs, an area that has gained popularity due to its accessibility and versatility.

Why should you be interested in open-source LLMs?

Open-source LLMs are freely available for anyone to access, use, modify, and distribute. They are designed to be more transparent, improve collaboration and accessibility. This allows a wider range of developers and researchers to contribute to the improvement of these AI models, which in turn helps to minimise biases and encourage innovation. They are leading the way in transforming how we process information, communicate, and approach problem solving. From startups to corporations, these models have a wide-ranging impact on various aspects of our digital lives.

However, given the abundance of tools, it can be challenging to determine which ones are truly valuable. That’s why we wrote this blog. Our parameters for picking up models are benchmarks and developer community preferences. We have carefully curated a list of the 10 open-source LLM tools that excel in their capabilities, community support, and innovative features. Whether you are a developer, a business owner, or simply someone passionate about technology, familiarising yourself with these tools can give you an edge.

What is a Large Language Model (LLM)?

A large language model, commonly known as LLM, is a type of artificial intelligence algorithm that uses deep learning techniques and vast amounts of data to understand, summarise, generate, and predict new content. LLMs are trained using large textual datasets and can recognise, translate, predict, or generate text or other content. They are also referred to as neural networks, which are computing systems inspired by the human brain.

LLMs, or large language models, are typically classified as open-source or proprietary.

The unique advantage of open-source LLMs is that they are accessible to everyone. This transparency allows us to explore the training data, understand their construction, and see how they function. Open-source LLMs encourage collaboration and innovation, enabling researchers and developers to contribute to and improve these models.

On the other hand, proprietary models like certain versions of GPT (except GPT 2) and Claude do not usually provide access to their trained weights or complete codebases, though they often share detailed information about their design and capabilities.

What is Open Weight LLM?

Open Weight Large Language Model refers to a model where the trained weights, or parameters, are publicly available. This allows developers and researchers to use, fine-tune, and experiment with a fully trained model without needing access to the original training data or process.

However, the underlying code and architecture might not be open, restricting modifications to the model’s structure or training methodology.

What is Open Source LLM?

The Open Source Large Language Model includes both the trained weights and the source code under an open-source license. This means that users have full access to the model’s architecture, training data, and processes, allowing comprehensive modifications, replications, and improvements.

Open-source LLMs aim for transparency, collaboration, and innovation by providing the complete framework and methodology behind the model.

Top 10 Open-source LLM

Choosing the right open-source language model can be hard. That is why we have curated this list of open-source LLMs. We have carefully selected the top 10 open-source LLMs, each with unique key features, using this foundation and our industry expertise of AI and LLMs. The list of open source LLMs follows below:

1. Llama 3

Introduction of Meta Llama 3: Meta Llama 3, the latest generation of the state-of-the-art open-source large language model, is introduced, with models soon available on various platforms including AWS, Google Cloud, Microsoft Azure, and more.
State-of-the-art Performance: Llama 3 aims to offer state-of-the-art performance with 8B and 70B parameter models, showcasing improvements in reasoning, code generation, and instruction following over previous iterations.
Development Focus of Llama 3: The development of Llama 3 focuses on model architecture enhancements, scaling up pretraining with a large dataset, instruction fine-tuning, and providing trust and safety tools such as Llama Guard 2, Code Shield, and CyberSec Eval 2.

Explore Llama 3 on Hugging Face

2. SnowFlake Arctic

Model Availability: Both base and instruct-tuned versions of Arctic are accessible under the Apache-2.0 license and can easily integrate into various research, prototype, and product development activities.
Comprehensive Documentation: Extensive resources, including tutorials and a live demo of the Streamlit app are provided in the Snowflake Arctic GitHub repository, ensuring ease of access and understanding.
Advanced Architecture: Arctic’s architecture combines a 10B dense transformer model with a residual 128×3.66B MoE MLP, resulting in 480B total and 17B active parameters, strategically selected using a top-2 gating approach.

Explore Snowflake Arctic on Hugging Face

3. Microsoft Phi 3

Microsoft introduces a family of small language and multi-modal models called Phi-3, available in various context lengths to suit diverse needs.

Versatile Models: Phi-3 offers models in different sizes and formats, from mini to medium, supporting both text and vision tasks.
Compact Design: With models ranging from 4k to 128k parameters, Phi-3 balances performance with efficiency.
Hugging Face Format: Phi-3 models in Hugging Face format cater to text generation tasks and are available in various sizes.

Explore Phi 3 on Hugging Face

4. Vicuna-33B

Fine-Grained Contextual Understanding: Captures intricate nuances of context for accurate responses.
Cross-Domain Versatility: Trained on diverse text sources for proficiency across various domains.
Rapid Inference Speed: Delivers fast responses without compromising accuracy.

Explore Vicuna-33B on Hugging Face

5. Cohere Command R+

📝Note: Cohere R+ weights are openly available. However, it is not an open-source LLM because they do not provide a license for commercial use; it is available for research use only.

Cutting-Edge AI Release: C4AI Command R+ emerges as an open weights research breakthrough, boasting a colossal 104 billion parameter model equipped with advanced capabilities like Retrieval Augmented Generation (RAG) and multi-step tool use for automating intricate tasks.
Multilingual: Command R+ undergoes rigorous evaluation across 10 languages, ensuring stellar performance in English, French, Spanish, Italian, German, Brazilian Portuguese, Japanese, Korean, Arabic, and Simplified Chinese.
Comprehensive Tool Use: This model revolutionises task execution with its pioneering tool utilisation, enabling multi-step tool use to tackle complex challenges effectively. With its refined conversational tool use capabilities, Command R+ integrates seamlessly into various workflows, ranging from reasoning to summarization and question answering.

Explore Cohere on Hugging Face

Suggested Reading

6. Stable Beluga 2

Model weights: StableLM 2 12B Chat is a 12 billion parameter instruction-tuned language model designed by Stability AI. It utilizes Direct Preference Optimization (DPO) and is trained on a mix of publicly available and synthetic datasets.
Performance: StableLM 2 12B Chat demonstrates competitive performance, scoring 8.15 ± 0.08 on the MT Bench (Inflection-corrected) evaluation, positioning it among top models in chat applications

Explore Stable Beluga 2 on Hugging Face

7. Qwen 2

Chinese Language Proficiency: specialises in understanding and generating Chinese content.

Cutting-Edge Model Series: Qwen2 is a new series of large language models, offering a wide range of base and instruction-tuned models from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repository focuses on the 0.5B Qwen2 base language model.

Performance Benchmark: Qwen2 surpasses many state-of-the-art open-source language models, including the previous Qwen1.5 release. It competes strongly against proprietary models across various benchmarks, spanning language understanding, generation, multilingual capability, coding, mathematics, and reasoning tasks.

Model Architecture: Built on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, and an improved tokenizer adaptive to multiple natural languages and codes.

Explore Qwen on Hugging Face

8. Cohere AI Aya

Multilingual Capability: Aya-23-8B shines in generating text of exceptional quality and coherence, covering diverse topics and languages.
Adaptability with Minimal Data: Demonstrates remarkable performance in natural language processing tasks even with limited training data, making it an efficient choice for various applications.
Seamless Integration: With a simple API, Aya-23-8B offers effortless integration into applications, ensuring accessibility and user-friendliness for developers and users alike.

Explore cohere aya on Hugging Face

9. Gemma

Dual-Sized Offering: Gemma offers two sizes tailored for diverse deployment scenarios. The 7B parameter model is ideal for efficient development on consumer-size GPUs and TPUs, while the 2B version caters to CPU and on-device applications. Both sizes are available in base and instruction-tuned variants.
Performance Overview: Gemma-7B is also a good option for top models in the 7B weight category. While Gemma-2B offers intriguing potential for its size, it may not match the leaderboard scores of similarly sized models.

Explore Gemma on Hugging Face

10. Mistral Codestral

Cutting-Edge Programming AI: Codestral-22B-v0.1 boasts training on an extensive dataset covering over 80 programming languages, including popular ones like Python, Java, C, C++, JavaScript, and Bash.
Versatile Usage: This model is versatile, capable of providing documentation, explanations, code factorization, and generating code based on specific instructions. It’s also proficient in predicting middle tokens between prefixes and suffixes, making it invaluable for software development enhancements like in VS Code.
Compatibility: Codestral-22B-v0.1 seamlessly integrates with the transformers library, allowing for straightforward usage within existing workflows.

Explore Mistral on Hugging Face

Each of these models has been designed with specific features that make them suitable for various applications in natural language processing, from text generation to complex problem-solving tasks.

Conclusion

In this blog, we looked at ten open-source language models. Each model, from Llama 2 to Mistral, has its own set of strengths and weaknesses. These tools are more than just techniques to grasp language; they represent a significant advancement in how we use technology and cope with complex information. Some of the models on the list are publicly available open-weight models, but they come with restricted access for commercial use.

The open-source nature of these models is critical. It enables greater access, collaboration, and the development of new AI ideas. Whether you’re a developer, researcher, or simply curious, these models provide numerous chances for learning, development, and application.

With these large language models, they will change things in a big way, from schools to businesses. We can work more efficiently, be more creative, and solve problems more effectively if we understand them and know how to use them. The future of AI is bright, and these models are leading the way!