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5 Best Open Source Large Language Models (LLMs) Redefining AI

Open Source Large Language Models

Welcome to our new blog ‘5 Best Open Source Large Language Models (LLMs) Redefining AI’. In the ever-evolving landscape of artificial intelligence (AI), Large Language Models (LLMs) have emerged as pivotal forces, revolutionizing technology and our interactions with it.

As LLMs grow in sophistication, democratizing access to them becomes crucial. Open-source models are leading this democratization charge, empowering researchers, developers, and enthusiasts to delve deep into their intricacies, fine-tune them for specific tasks, and even build upon their foundations.

In this article, we’ll delve into the realm of top-tier open source large language models (LLMs) that are leaving a lasting impact on the AI community. Each model brings its distinct strengths and capabilities to the forefront.

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5 Open Source Large Language Models (LLMs)

1. Llama 2: Breaking Boundaries in AI

Meta’s Llama 2 is a groundbreaking addition to their AI model lineup. It goes beyond just being another model; it’s engineered to power a spectrum of cutting-edge applications. With its extensive and diverse training data, Llama 2 has made substantial progress compared to its predecessor. This diversity ensures that Llama 2 is not just an incremental upgrade, but a monumental leap into the future of AI-driven interactions.

The collaboration between Meta and Microsoft has expanded Llama 2’s horizons. This open-source model is now available on platforms like Azure and Windows, giving developers and organizations the tools to craft generative AI experiences. This partnership underscores both companies’ commitment to making AI accessible and inclusive.

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Llama 2 is more than just an upgrade; it’s a paradigm shift in the chatbot landscape. Unlike the original Llama model, which had limited availability to prevent misuse, Llama 2 aims for a broader audience. It’s optimized for platforms like AWS, Azure, and Hugging Face’s AI model hosting platform. Furthermore, the Meta-Microsoft collaboration positions Llama 2 not only on Windows but also on devices powered by Qualcomm’s Snapdragon system-on-chip.

Safety is at the core of Llama 2’s design. Learning from the challenges faced by previous large language models, Meta has taken extensive precautions to ensure Llama 2’s reliability. Rigorous training has minimized ‘hallucinations,’ misinformation, and biases.

Key Attributes of Llama 2:

  • Diverse Training Data: Llama 2’s training data is extensive and diverse, leading to comprehensive understanding and performance.
  • Microsoft Collaboration: Llama 2 is supported on Azure and Windows, expanding its scope of application.
  • Wider Availability: Unlike its predecessor, Llama 2 is accessible to a broader audience and is adaptable on various platforms.
  • Safety-Centric Design: Emphasis on safety ensures Llama 2 produces accurate, reliable results while minimizing harmful outputs.
  • Optimized Versions: Llama 2 offers two main versions – Llama 2 and Llama 2-Chat, the latter tailored for two-way conversations with complexity ranging from 7 billion to 70 billion parameters.
  • Enhanced Training: With training on two million tokens, Llama 2 far surpasses the original Llama’s 1.4 trillion tokens.

2. Claude 2: Elevating AI Capabilities

Anthropic’s latest AI model, Claude 2, signifies more than an upgrade; it marks a significant advancement in AI model capabilities. Designed to provide users with extensive and coherent responses, Claude 2 boasts enhanced performance metrics. It’s accessible through an API and a dedicated beta website. Users find interactions with Claude intuitive, as it provides detailed explanations and demonstrates extended memory capacity.

Claude 2’s academic and reasoning capabilities are commendable. Achieving a score of 76.5% in the multiple-choice section of the Bar exam, it surpasses Claude 1.3’s 73.0%. Against college students preparing for graduate programs, Claude 2 scores above the 90th percentile in GRE reading and writing exams, showcasing its prowess in comprehending and generating intricate content.

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Claude 2’s versatility stands out. Processing inputs of up to 100K tokens, it can review extensive documents ranging from technical manuals to comprehensive books. It effortlessly produces extended documents, from official communications to detailed narratives. Coding capabilities are also refined, evident from its scores on coding assessments.

Safety remains paramount for Anthropic. Claude 2 is engineered to produce benign responses, addressing concerns about harmful or inappropriate content.

Key Features of Claude 2:

  • Enhanced Performance: Faster responses and more detailed interactions make Claude 2 stand out.
  • Multiple Access Points: Claude 2 can be accessed via API or its dedicated beta website,
  • Academic Excellence: Claude 2 excels in academic evaluations, particularly in GRE reading and writing.
  • Extended Input/Output: Handling inputs of up to 100K tokens, Claude 2 produces comprehensive documents in a single session.
  • Advanced Coding: Its coding skills have improved, demonstrated through coding and mathematical evaluation scores.
  • Safety Protocols: Claude 2 prioritizes safety through evaluations and advanced techniques.
  • Expansion Plans: Future plans include expanding Claude 2’s availability globally.

3. MPT-7B: Shaping the Landscape

MosaicML Foundations has made a remarkable contribution with MPT-7B, their latest open-source LLM. MPT-7B, or MosaicML Pretrained Transformer, is a GPT-style, decoder-only transformer model. Notable enhancements include optimized layer implementations and architectural changes for better training stability.

A standout feature is MPT-7B’s training on a vast dataset of 1 trillion tokens, executed on the MosaicML platform over 9.5 days. Its open-source nature positions it as a valuable tool for commercial applications, with potential to impact predictive analytics and decision-making processes.

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In addition to the base model, specialized models like MPT-7B-Instruct and MPT-7B-Chat cater to specific tasks. MPT-7B’s development journey was comprehensive, involving stages from data preparation to deployment.

Key Attributes of MPT-7B:

  • Commercial Value: Licensed for commercial use, MPT-7B benefits businesses.
  • Extensive Training: Trained on a vast dataset of 1 trillion tokens.
  • Handling Lengthy Inputs: Designed to process lengthy inputs without compromise.
  • Efficiency: Optimized for swift training and inference, delivering timely results.
  • Open Source: MPT-7B comes with efficient open-source training code, promoting transparency.
  • Superiority: Outperforms other models in the 7B-20B range, matching LLaMA-7B’s quality.

4. Falcon: Ascending the LLM Hierarchy

Falcon LLM, particularly Falcon-40B, rapidly ascends the LLM hierarchy. With 40 billion parameters, trained on one trillion tokens, Falcon-40B operates as an autoregressive decoder-only model. This architecture exhibits superior performance to GPT-3 with efficient use of resources.

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Falcon’s emphasis on data quality during development is notable. A robust data pipeline, scaling to thousands of CPU cores, ensures high-quality content extraction. Falcon-7B and specialized models extend Falcon’s capabilities.

Training Falcon-40B involved meticulous data curation, resulting in quality content. Validation against open-source benchmarks affirms its excellence.

Key Attributes of Falcon LLM:

  • Extensive Parameters: Falcon-40B’s 40 billion parameters enable comprehensive learning.
  • Autoregressive Model: Predicting tokens based on preceding ones, akin to GPT.
  • Performance Excellence: Superior to GPT-3 with efficient resource utilization.
  • Quality Data Extraction: Robust data pipeline extracts high-quality content.
  • Diverse Models: Falcon-7B and specialized versions enhance its capabilities.
  • Open Source: Falcon LLM is open-sourced, fostering accessibility and inclusivity.

5. Vicuna-13B: Enriching Open-Source LLMs

LMSYS ORG’s Vicuna-13B makes a significant impact in the open-source LLM domain. Fine-tuned using user-shared conversations from ShareGPT, Vicuna-13B achieves over 90% quality of models like OpenAI ChatGPT and Google Bard.

Outperforming models like LLaMA and Stanford Alpaca, Vicuna-13B’s training was cost-effective. Its code, weights, and online demo are available for non-commercial exploration.

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Fine-tuned with 70K user-shared ChatGPT conversations, Vicuna-13B generates detailed, well-structured responses comparable to ChatGPT.

Key Attributes of Vicuna-13B:

  • Open Source: Vicuna-13B is publicly accessible, promoting transparency and engagement.
  • Extensive Training Data: Training on 70K conversations enables understanding of diverse interactions.
  • Competitive Performance: Vicuna-13B’s quality rivals ChatGPT and Google Bard.
  • Cost-Effective: Trained at a low cost of around $300.
  • Fine-Tuning: Tailored on LLaMA for enhanced performance.
  • Online Demo: Interactive demo lets users experience Vicuna-13B’s capabilities.

Expanding Horizons of LLMs

Open-source LLMs exemplify collaborative efforts in AI. From Vicuna’s adept chatbot skills to Falcon’s performance metrics, these models represent the current zenith of LLM technology. As AI advances, open-source models will continue shaping its future.

Whether you’re a researcher, enthusiast, or curious explorer, these models offer a world of possibilities worth exploring.

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Comparison Table of Best 5 Open Source Large Language Models (LLMs)

Here’s a detailed comparison table of the five open-source Large Language Models (LLMs) discussed:

FeatureLlama 2Claude 2MPT-7BFalcon LLMVicuna-13B
OrganizationMetaAnthropicMosaicML FoundationsTechnology Innovation InstituteLMSYS ORG
ParametersUp to 70BNot specifiedUp to 1TUp to 40B13B
AvailabilityOpen SourceOpen SourceOpen SourceOpen SourceOpen Source
Use CasesAI interactionsExtended responsesCommercial appsSuperior performanceChatbot responses
Microsoft Collab.YesNoNoNoNo
Training DataVast and variedNot specified1T tokens1T tokens70K convos
Safety MeasuresExtensive measuresSafety emphasisNot specifiedData quality focusData quality focus
Specialized VersionsYesNoYesYesNo
Performance MetricsNot specifiedGRE scoresSuperior performanceEfficient resource useComparable to ChatGPT
Cost of TrainingNot specifiedNot specifiedNot specifiedLow costNot specified
Available PlatformsAzure, WindowsNot specifiedNot specifiedNot specifiedNot specified
Access PointsAPI, platformsAPI, beta websiteOpen SourceOpen SourceDemo, code, weights

Please note that some information might not be available or specified in the original content for certain features of each LLM. This table is based on the provided information and aims to highlight key characteristics of each model for comparison.

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Conclusion: Shaping the Future of AI with Open-Source LLMs

As the world of artificial intelligence continues its rapid evolution, open-source Large Language Models (LLMs) stand as beacons of innovation, driving advancements that reshape our interactions with technology. The five remarkable LLMs discussed in this exploration – Llama 2, Claude 2, MPT-7B, Falcon LLM, and Vicuna-13B – each bring a unique set of capabilities that underscore the collaborative and progressive nature of the AI community.

Llama 2, with its revolutionary design and diverse training data, signals a monumental step forward in AI-driven interactions. Its partnership with Microsoft broadens the horizon of possibilities, making it accessible on platforms like Azure and Windows, ultimately democratizing the potential of AI.

Claude 2, equipped with extended responses and academic excellence, demonstrates its prowess in understanding and generating complex content. Its commitment to safety ensures reliable outputs while its online accessibility fosters exploration by enthusiasts and experts alike.

MPT-7B, a creation of MosaicML Foundations, highlights the power of a comprehensive training dataset and its potential to impact commercial applications. With open-source availability, MPT-7B becomes a tool that can transform predictive analytics and decision-making processes.

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Falcon LLM’s efficient resource utilization and data quality emphasis exemplify its dedication to performance. Its various versions cater to specialized tasks, making it a versatile choice for a range of applications.

Vicuna-13B, the open-source chatbot from LMSYS ORG, displays competitive prowess in generating detailed responses. Its availability for public exploration offers a glimpse into the potential of AI-driven conversations.

As we navigate the landscape of AI, these open-source LLMs stand as testament to the collective effort of researchers, developers, and organizations in pushing the boundaries of technology. The journey ahead promises even more innovation and exploration, fueled by the spirit of collaboration and the pursuit of excellence. As we continue to witness the impact of these models, the future of AI looks brighter and more accessible than ever before.


Here are some frequently asked questions (FAQs) about the 5 open source Large Language Models (LLMs) discussed:

1. Llama 2:

Q1: What is Llama 2? A: Llama 2 is an open source Large Language Model (LLM) developed by Meta. It’s designed to power a range of AI applications, offering improved capabilities over its predecessor.

Q2: How does Llama 2 differ from the original Llama model? A: Llama 2 represents a significant advancement over the original Llama model. It offers diverse training data, broader availability, and safety measures to minimize misinformation and biases.

Q3: What platforms is Llama 2 available on? A: Llama 2 is supported on platforms like Azure and Windows, owing to a collaboration between Meta and Microsoft. This aims to make the model more accessible to developers and organizations.

Q4: What are the key features of Llama 2? A: Llama 2’s features include diverse training data, Microsoft collaboration, open availability, safety-centric design, and optimized versions for various platforms.

2. Claude 2:

Q1: What is Claude 2 and how is it different from other models? A: Claude 2 is an AI model developed by Anthropic. It offers enhanced performance metrics, academic excellence, and extended input/output capabilities. It outperforms its predecessor, Claude 1.3.

Q2: What are the academic capabilities of Claude 2? A: Claude 2 has achieved commendable results in academic evaluations, such as scoring above the 90th percentile in GRE reading and writing exams. It also performs well in reasoning tasks.

Q3: How does Claude 2 ensure safety in its responses? A: Anthropic has emphasized safety in Claude 2’s design to minimize harmful or inappropriate content. Rigorous evaluations and advanced safety techniques are employed.

Q4: Can I access Claude 2 for experimentation? A: Yes, Claude 2 can be accessed via an API or its dedicated beta website, This allows users to experience its capabilities and interactions.

3. MPT-7B:

Q1: What is MPT-7B and what makes it unique? A: MPT-7B is an open-source LLM developed by MosaicML Foundations. It’s a GPT-style transformer model with enhancements in layer implementations and training stability.

Q2: How was MPT-7B trained and what datasets were used? A: MPT-7B was trained on an extensive dataset of 1 trillion tokens using the MosaicML platform over a span of 9.5 days. The training data comprises text and code.

Q3: What are the commercial applications of MPT-7B? A: MPT-7B is licensed for commercial use and holds the potential to impact predictive analytics and decision-making processes in businesses and organizations.

4. Falcon:

Q1: What is Falcon LLM and how does it stand out? A: Falcon LLM, developed by the Technology Innovation Institute, is a model equipped with 40 billion parameters. It operates as an autoregressive decoder-only model and outperforms GPT-3 with efficient resource utilization.

Q2: How does Falcon ensure data quality during development? A: The Technology Innovation Institute placed emphasis on data quality by constructing a robust data pipeline for high-quality content extraction from the web.

Q3: What other versions of Falcon LLM are available? A: In addition to Falcon-40B, other versions like Falcon-7B and specialized models such as Falcon-40B-Instruct are available, catering to specific tasks.

5. Vicuna-13B:

Q1: What is Vicuna-13B and how is it trained? A: Vicuna-13B is an open-source chatbot model developed by LMSYS ORG. It’s fine-tuned using user-shared conversations sourced from ShareGPT to generate detailed and well-structured responses.

Q2: How does Vicuna-13B compare to other established chatbots? A: Vicuna-13B achieves over 90% quality compared to renowned models like OpenAI ChatGPT and Google Bard, demonstrating competitive performance.

Q3: Is Vicuna-13B available for public use? A: Yes, Vicuna-13B is open-source and publicly accessible. Its code, weights, and an online demo are available for non-commercial exploration.

Please note that these FAQs are based on the provided information and aim to provide general answers to common questions about each of the discussed open-source LLMs.

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Shivani Rohila

Multifaceted professional: CS, Lawyer, Yoga instructor, Blogger. Passionate about Neuromarketing and AI.🤖✍️ I embark on a journey to demystify the complexities of AI for readers at all levels of expertise. My mission is to share insights, foster understanding, and inspire curiosity about the limitless possibilities that AI brings to our ever-evolving world. Join me as we navigate the realms of innovation, uncovering the transformative power of AI in shaping our future.

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