Large Language Model commonly known as an LLM, refers to a neural network equipped with billions of parameters and trained extensively on extensive datasets of unlabeled text. This training typically involves self-supervised or semi-supervised learning techniques. In this article, we explore about Top 20 LLM Models and get to know how each model has distinct features and applications.
Top 20 LLM Model
1. GPT-4
As of 2024, OpenAI’s GPT-4 stands out as the leading AI Large Language Model (LLM) in the market. Launched in March 2023, its parameter count has not been released to the public, though there are rumors that the model has more than 170 trillion but GPT-4 has demonstrated exceptional capabilities, excelling in complex reasoning, advanced coding, proficiency in various academic domains, and achieving human-level performance in diverse skills. Notably, it’s the first multimodal model accepting both text and image inputs. GPT-4 distinguishes itself by addressing hallucination issues and significantly improving factuality. In factual evaluations across multiple categories, GPT-4 outperforms ChatGPT-3.5, scoring close to 80%. OpenAI has also prioritized aligning GPT-4 with human values, employing Reinforcement Learning from Human Feedback (RLHF) and rigorous adversarial testing by domain experts.
Features of GPT-4
- Massive Scale: GPT-4 boasts a colossal architecture, allowing it to process vast amounts of data and generate highly coherent and contextually relevant text.
- Advanced Natural Language Understanding: It exhibits enhanced capabilities in understanding complex language structures, nuances, and context, leading to more accurate and contextually appropriate responses.
- Fine-Tuning Flexibility: GPT-4 offers flexibility in fine-tuning for specific tasks or domains, making it adaptable to various NLP applications.
2. GPT-3
GPT-3 is an OpenAI large language model, released in 2020 stands out as a groundbreaking NLP model, boasting a record-breaking 175 billion parameters—the highest among NLP models. With its colossal size, GPT-3 has revolutionized natural language processing, showcasing the capability to generate human-like responses across prompts, sentences, paragraphs, and entire articles. Employing a decoder-only transformer architecture, GPT-3 represents a significant leap, being 10 times larger than its predecessor. In a noteworthy development, Microsoft announced exclusive use of GPT-3’s underlying model in September 2022. GPT-3 marks the culmination of the GPT series, introduced by OpenAI in 2018 with the seminal paper “Improving Language Understanding by Generative Pre-Training.”
Features of GPT-3
- Unprecedented Size: GPT-3 is renowned for its sheer size, containing billions of parameters that contribute to its impressive language generation capabilities.
- Zero-Shot Learning: It can perform tasks without explicit training on them, showcasing its ability to generalize across a wide range of NLP tasks.
- Contextual Understanding: GPT-3 excels in understanding and maintaining context over long passages of text, resulting in coherent and contextually relevant responses.
3. GPT-3.5
GPT-3.5 represents an enhanced iteration of GPT-3, featuring a reduced parameter count. This upgraded version underwent fine-tuning through reinforcement learning from human feedback, demonstrating OpenAI’s commitment to refining language models. Notably, GPT-3.5 serves as the underlying technology for ChatGPT, with various models available, including the highly capable GPT-3.5 turbo, as highlighted by OpenAI. It’s an incredibly fast model and generates a complete response within seconds and it’s also free to use without any daily restrictions. But it does have some shortcomings like it can be prone to hallucinations, sometimes generating incorrect information. This makes it a little less ideal for serious research work. In the HumanEval benchmark, the GPT-3.5 model scored 48.1% .
Feature of GPT-3.5
- Performance Improvements: Building upon GPT-3, GPT-3.5 incorporates enhancements in performance metrics such as accuracy, efficiency, and speed.
- Efficient Fine-Tuning: It offers improved fine-tuning capabilities, allowing users to tailor the model for specific tasks or datasets with ease.
- Scalability: GPT-3.5 maintains scalability, enabling it to handle large-scale datasets and generate high-quality text across diverse applications.
4. Gemini
Google’s new AI, Gemini, seems to be stepping up the game against ChatGPT. Released in December 2023 it was built from the ground up to be multimodal, which means it can generalize and seamlessly understand, operate across and combine different types of information including text, code, audio, image and video. It has outperformed ChatGPT in almost all academic tests, like understanding text, images, videos, and even speech. With a score of 90.0%, Gemini Ultra is the first model to outperform human experts on MMLU (massive multitask language understanding), which uses a combination of 57 subjects such as math, physics, history, law, medicine and ethics for testing both world knowledge and problem-solving abilities. Developers and enterprise customers can access Gemini Pro via the Gemini API in Google AI Studio or Google Cloud Vertex AI.
Feature of Gemini
- Conversational AI Focus: Gemini emphasizes improving conversational AI by better understanding context and generating more human-like responses in dialogues.
- Contextual Sensitivity: It exhibits enhanced sensitivity to context shifts within conversations, leading to more coherent and contextually appropriate responses.
- Multimodal Integration: Gemini integrates multiple modalities such as text, images, and audio to enrich the conversational experience and generate more comprehensive responses.
5. LLaMA
LLaMa, or Large Language Model Meta AI, emerges as a significant development in the realm of Large Language Models by Meta AI. The inaugural release of LLaMa in February 2023, with the largest version of 65 billion parameters in size. Since its unveiling, Meta’s introduction of the LLaMa family of large language models (LLMs) has become a valuable asset for the open-source community. The diverse range of LLaMA models, spanning from 7 billion to 65 billion parameters, has demonstrated superior performance compared to other LLMs, including GPT-3, across various benchmarks. An undeniable advantage of LLaMA models lies in their open-source nature, empowering developers to easily fine-tune and create new models tailored to specific tasks. This approach fosters rapid innovation within the open-source community, leading to the continuous release of new and enhanced LLM models.
Feature of Llama
- Long-Term Language Understanding: LLaMA specializes in long-term language understanding and reasoning, enabling it to grasp complex relationships and concepts across extended passages of text.
- Reasoning Capabilities: It incorporates advanced reasoning capabilities, allowing it to infer implicit information, draw logical conclusions, and answer complex questions.
- Contextual Memory: LLaMA retains contextual memory over prolonged interactions, facilitating more coherent and contextually consistent responses in dialogues and conversations.
6. PaLM 2 (Bison-001)
PaLM 2 is a large language model (LLM) developed by Google AI. Google has elevated the capabilities of PaLM 2 by emphasizing various aspects, including commonsense reasoning, formal logic, mathematical equations, and advanced coding, spanning over 20 languages. Remarkably, the most extensive version of PaLM 2 is purportedly trained on a massive 540 billion parameters. With its multilingual proficiency, PaLM 2 excels in comprehending idioms, solving riddles, and interpreting nuanced texts in a diverse range of languages, a feat that poses challenges for other Large Language Models (LLMs). One more advantage of PaLM 2 is that it’s very quick to respond and offers three responses at once.
Feature of PaLM 2 (Bison-001)
- Pattern-Based Learning: PaLM 2 utilizes pattern-based learning techniques to enhance text generation and comprehension, enabling it to capture intricate language patterns and nuances.
- Adaptive Training: It offers adaptive training capabilities, allowing it to continuously improve and refine its language understanding and generation abilities over time.
- Efficiency: PaLM 2 prioritizes efficiency in processing and resource utilization, making it suitable for a wide range of NLP applications, including those with resource constraints.
7. Bard
Google Bard stands as an experimental conversational AI service driven by LaMDA (Language Model for Dialogue Applications), a project undertaken by Google AI. Notably, Bard introduces subtle distinctions from other Large Language Models in its approach. Firstly, it is tailored for natural conversations, enabling seamless dialogue with users. Secondly, Bard is internet-connected, allowing real-time access and processing of information from the online domain. This unique feature positions Bard to provide more current and pertinent information compared to LLMs trained on static datasets. With an impressive 1.6 trillion parameters, BARD emerges as an extraordinary language model with a remarkable capacity to discern intricate language nuances and patterns.
Features of Bard
- Flexibility and Efficiency: Bard is highly flexible and efficient, accommodating various NLP tasks and workflows while maintaining robust performance.
- Large-Scale Architecture: It features a large-scale architecture, enabling it to handle extensive datasets and complex language structures with ease.
- Fine-Tuning Capabilities: Bard offers fine-tuning capabilities, allowing users to adapt the model for specific tasks or domains and achieve optimal performance.
8. Claude v1
It’s not as popular LLM as compared to GPT or Llama but Claude is a powerful LLM developed by Anthropic, former co-founded by OpenAI employees. It’s a newbie on the Large Language Model block that’s outperforming PaLM 2 in benchmark tests and offering a 100k token context window for the first time ever. It’s in competition with GPT-4 and scored 7.94 in the MT-Bench test, while GPT-4 scored 8.99 and in the MMLU benchmark as well, Claude v1 secures 75.6 points, and GPT-4 scores 86.4. Previously said that is offers 100k tokens which is equivalent to about 75,000 words, this means that you can easily load a full-length book into Claude’s context window, it would still understand it and create text in response to your prompts.
Features of Claude v1
- Understanding Complex Structures: Claude v1 excels in understanding complex language structures, including nuanced expressions, idiomatic phrases, and syntactic variations.
- Coherent Responses: It generates coherent and contextually relevant responses across diverse contexts, maintaining coherence and consistency in dialogues and interactions.
- Task Adaptability: Claude v1 is adaptable to various NLP tasks and domains, offering flexibility in application and integration into different workflows and systems.
9. Falcon
The Falcon is a causal decoder-only model developed by the Technology Innovation Institute(TII), UAE stands out as a dynamic and scalable language model, offering exceptional performance and scalability. Its an open source model which has outranked all the other open-source models released so far, including LLaMA, StableLM, MPT, and more. Notably, Falcon LLM underwent training (on AWS Sagemaker) on an extensive dataset comprising web text and curated sources. The training process incorporated custom tooling and a unique data pipeline to ensure the quality of the training data. This model incorporates enhancements like rotary positional embeddings and multi-query attention, contributing to its improved performance. The Falcon model has been primarily trained in English, German, Spanish, and French but it can also work in many other languages too.
Features of Falcon
- Efficiency and Scalability: Falcon prioritizes efficiency and scalability, making it suitable for large-scale deployment and processing of vast amounts of data.
- Task Optimization: It is optimized for various NLP tasks, including text classification, language generation, and sentiment analysis, delivering high-quality results across different applications.
- Model Compression: Falcon incorporates techniques for model compression and optimization, reducing memory and computational requirements without compromising performance.
10. Cohere
Cohere founded by former Google employees who worked on the Google Brain team. Cohere is an enterprise LLM that can be custom-trained and fine-tuned to a specific company’s use case. Cohere has a multiple models ranging from having just 6B parameters to large models trained on 52B parameters. The Cohere Command model is earning acclaim for its precision and resilience, securing the top position for accuracy according to Stanford HELM. Noteworthy companies, including Spotify, Jasper, HyperWrite, and more, are leveraging Cohere’s model to enhance their AI experiences. But it is charging $15 to generate 1 million tokens which is very high when compared to its competitors.
Features of Cohere
- Contextual Understanding: Cohere focuses on contextual understanding, capturing nuanced relationships and dependencies within text to generate more accurate and contextually relevant responses.
- Conversational AI Enhancement: It enhances conversational AI by better understanding user intents, preferences, and context shifts, leading to more engaging and human-like interactions.
- Multi-Turn Dialogue Handling: Cohere is proficient in handling multi-turn dialogues, maintaining coherence and context continuity over extended interactions to facilitate natural and fluid conversations.
11. Orca
Orca, a creation of Microsoft boasting 13 billion parameters, is strategically designed to operate efficiently even on a laptop. Notably, Orca is a fine-tuned version of Llama 2 that performs as well as or better than models that contain 10x the number of parameters. Remarkably, Orca achieves comparable performance to GPT-4 but with a considerably lower parameter count. It demonstrates proficiency on par with GPT-3.5 across various tasks. Orca 2 uses a synthetic training dataset and a new technique called Prompt Erasure to achieve this performance. The Orca 2 models employ a teacher-student training approach, leveraging a larger, more potent Large Language Model (LLM) as a teacher guiding a smaller student LLM. This strategy aims to elevate the performance of the student model to rival that of larger counterparts, optimizing the learning process.
Features of Orca
- Multimodal Integration: Orca integrates multiple modalities such as text, images, and audio to enrich language understanding and generation, enabling more comprehensive and contextually relevant responses.
- Cross-Modal Learning: It leverages cross-modal learning techniques to extract meaningful correlations between different modalities and enhance overall understanding and representation learning.
- Enhanced Contextual Sensitivity: Orca exhibits enhanced sensitivity to contextual cues across different modalities, allowing it to generate more accurate and contextually appropriate responses in multimodal settings.
12. Guanaco
Guanaco is also one the model that is derived from the framework of the existing model LLama. Guanaco is an open-source model tailored for contemporary chatbots, come in various sizes from 7B to 65B, with Guanaco-65B standing out as the most powerful, closely trailing the Falcon model in open-source performance. In the MMLU test, it scored 52.7 whereas the Falcon model scored 54.1. All the models of Guanaco are trained on the OASST1 dataset by Tim Dettmers, these models utilize a novel fine-tuning technique called QLoRA, optimizing memory usage without compromising task performance. Notably, Guanaco models surpass some top proprietary LLMs like GPT-3.5 in performance.
Features of Guanaco
- Unsupervised Learning: Guanaco specializes in unsupervised learning, leveraging large-scale unlabeled data to learn rich representations and generate contextually relevant text without explicit supervision.
- Semantic Understanding: It demonstrates advanced semantic understanding, capturing underlying meanings and intents within text to generate coherent and contextually appropriate responses.
- Adaptive Learning: Guanaco continuously adapts and refines its language understanding and generation abilities through self-supervised learning techniques, improving performance over time without additional labeled data.
13. Vicuna
Vicuna, an impactful open-source Large Language Model (LLM) stemming from LLaMa, has been crafted by LMSYS and fine-tuned with data from sharegpt.com( a portal where users share their ChatGPT conversations). The training dataset consists of 70,000 user-shared ChatGPT conversations, providing a rich source for honing its language abilities. Remarkably, the entire training process was achieved with a cost of only $300, accomplished with PyTorch FSDP on 8 A100 GPUs, was completed in just one day, showcasing the model’s efficiency in delivering high performance on a budget. In LMSYS’s own MT-Bench test, it scored 7.12 whereas the best proprietary model, GPT-4 secured 8.99 points. While smaller and less capable than GPT-4 based on various benchmarks, Vicuna performs admirably for its size, boasting 33 billion parameters compared to the trillions in GPT-4.
Features of Vicuna
- Efficient Training: Vicuna employs efficient training techniques, enabling rapid convergence and training on large-scale datasets with minimal computational resources.
- Robust Performance: It delivers robust performance across various NLP tasks, including text generation, summarization, and language understanding, achieving state-of-the-art results in benchmark evaluations.
- Scalability and Adaptability: Vicuna maintains scalability and adaptability, making it suitable for deployment in diverse environments and applications, from research prototypes to production systems.
14. MPT-30B
MPT-30B is a commercial Apache 2.0 licensed, open-source foundation model that exceeds the quality of GPT-3 (from the original paper) and is competitive with other open-source models such as LLaMa-30B and Falcon-40B. It is fine-tuned on a massive corpus of data from different sources, including GPTeacher, Baize, and even Guanaco. This model also has one of the longest context lengths (8K tokens). Additionally, it outperforms the GPT-3 model by OpenAI and scores 6.39 in LMSYS’s MT-Bench test. There are various MPT-30B models available, each with distinctive features. The models provides various options for model configuration and parameter tuning, allowing users to optimize their models for specific requirements.
Features of MPT-30B
- Multimodal Understanding: MPT-30B specializes in multimodal understanding, integrating text with other modalities such as images and audio to generate more comprehensive and contextually relevant responses.
- Cross-Modal Knowledge Transfer: It leverages cross-modal knowledge transfer techniques to enhance understanding and representation learning across different modalities, improving overall performance in multimodal tasks.
- Fine-Grained Contextual Sensitivity: MPT-30B exhibits fine-grained contextual sensitivity, capturing subtle nuances and dependencies within and across modalities to generate more accurate and contextually appropriate responses
15. 30B Lazarus
Unveiled in 2023 by CalderaAI, 30B-Lazarus stands out as an upgraded iteration of the LlaMA language model. Leveraging LoRA-tuned datasets from diverse models, the developer crafted a solution adept at excelling across various LLM benchmarks. It scored 81.7 in HellaSwag and 45.2 in MMLU, just after Falcon and Guanaco.This specific LLM ranks among the top open-source models for text generation, showcasing exceptional performance. It’s important to note that while it excels in text generation, it doesn’t support conversational, human-style chat. Multiple versions of the model cater to specific use cases across diverse industries.
Features of 30B Lazarus
- Scalability and Adaptability: 30B Lazarus emphasizes scalability and adaptability, enabling efficient training and deployment on large-scale datasets and diverse environments.
- Continual Learning: It supports continual learning, allowing the model to adapt and improve over time with new data and experiences, without the need for retraining from scratch.
- Robustness to Concept Drift: 30B Lazarus exhibits robustness to concept drift, maintaining performance and reliability in dynamic environments where data distributions may change over time.
16. Flan-T5
Flan-T5 emerges as a commercially available open-source LLM, introduced by Google researchers. Functioning as an encoder-decoder model, Flan-T5 undergoes pre-training across a spectrum of language tasks. The training regimen involves both supervised and unsupervised datasets, aiming to master mappings between sequences of text, essentially operating in a text-to-text paradigm. Flan-T5 comes in various sizes, Flan-T5-Large, which has 780M parameters which can manage over 1000 tasks. FLAN’s various models can support everything from commonsense reasoning to question generation and cause and effect classification. The technology can even detect “toxic” language in conversations and respond to various languages.
Features of Flan-T5
- Task-Specific Optimization: Flan-T5 focuses on task-specific optimization, fine-tuning the model for specific NLP tasks such as question answering, summarization, and text classification to achieve superior performance.
- Efficient Inference: It prioritizes efficiency in inference, delivering fast and responsive results without compromising on accuracy or quality, making it suitable for real-time applications and systems.
- Model Compression: Flan-T5 incorporates techniques for model compression and optimization, reducing memory and computational requirements for deployment in resource-constrained environments such as mobile devices and edge devices.
17. WizardLM
WizardLM, is also an open-source large language model which excels in comprehending and executing complex instructions. Employing the innovative Evol-instruct approach, a team of AI researchers rewrites initial instructions into more intricate forms, using the generated instruction data for fine-tuning the LLaMA model. This unique methodology enhances WizardLM’s performance on benchmarks, earning user preference over ChatGPT responses. Notably, in the MT-Bench test, WizardLM achieved a score of 6.35 points and 52.3 in the MMLU test. Despite its 13B parameters, WizardLM delivers impressive results, paving the way for more efficient and compact models.
Features of WizardLM
- Human-Computer Interaction Enhancement: WizardLM aims to enhance human-computer interactions by generating informative and contextually relevant responses, facilitating more natural and engaging dialogues and interactions.
- Multi-Turn Dialogue Handling: It is proficient in handling multi-turn dialogues, maintaining coherence and context continuity over extended interactions to enable fluid and seamless conversations.
- Interactive Learning: WizardLM supports interactive learning, allowing users to provide feedback and guidance during interactions to improve the model’s responses and adapt to user preferences over time.
18. Alpaca 7B
Alpaca, a standout in the Llama family, excels in language understanding and generation. Developed by Stanford University, this Generative AI chatbot is noted for its qualitative similarity to OpenAI’s GPT-3.5. What sets it apart is its cost-effectiveness, requiring less than $600 for creation. The spotlight is on Alpaca 7B, a fine-tuned version of Meta’s seven billion-parameters LLaMA language model. Hinging on techniques like mixed precision and Fully Sharded Data Parallel training, this LLaMA model was fine-tuned in just three hours on eight 80GB Nvidia A100 chips, costing less than $100 on cloud computing providers. Alpaca’s performance is claimed to be quantitatively comparable to OpenAI’s text-davinci-003. The evaluation was conducted using a self-instruct evaluation set, where Alpaca reportedly won 90 out of 89 comparisons against text-DaVinci-003.
Features of Alpaca 7B
- Efficient Architecture: Alpaca 7B features an efficient architecture, balancing performance and resource utilization to deliver high-quality results with minimal computational overhead.
- Task Adaptability: It is adaptable to various NLP tasks and domains, offering flexibility in application and integration into different workflows and systems.
- Robust Performance: Alpaca 7B maintains robust performance across diverse applications, demonstrating consistent accuracy and reliability in benchmark evaluations and real-world scenarios.
19. LaMDA
LaMDA, introduced as the successor to Google’s Meena in 2020, represents a significant leap in conversational AI. Unveiled during the 2021 Google I/O keynote, LaMDA relies on the powerful Transformer architecture, a neural network model pioneered and open-sourced by Google Research in 2017. The training process for LaMDA is extensive, involving a vast dataset of billions of documents, dialogs, and utterances, totaling a staggering 1.56 trillion words. Google emphasizes that LaMDA’s responses are crafted to be “sensible, interesting, and specific to the context.” LaMDA’s capabilities extend to access multiple symbolic text processing systems, including a database, real-time clock and calendar, mathematical calculator, and natural language translation system. This versatility grants LaMDA superior accuracy in tasks supported by these systems, positioning it as one of the pioneering dual-process chatbots in the field of conversational AI.
Features of LaMDA
- Conversational AI Enhancement: LaMDA focuses on improving conversational AI by better understanding nuances and context in human communication, leading to more engaging and human-like interactions.
- Sensitive to Context Shifts: It exhibits enhanced sensitivity to context shifts within conversations, enabling it to generate more coherent and contextually appropriate responses in dynamic dialogue settings.
- Semantic Understanding: LaMDA demonstrates advanced semantic understanding, capturing underlying meanings and intents within text to generate more accurate and contextually relevant responses.
20. BERT
Last but not the least, BERT, or Bidirectional Encoder Representations from Transformers, is a groundbreaking open-source model introduced by Google in 2018. As one of the pioneers among Large Language Models (LLMs), BERT quickly established itself as a standard in Natural Language Processing (NLP) tasks. Its impressive performance made it a go-to choice for various language-related applications, including general language understanding, question answering, and named entity recognition. BERT’s success can be attributed to its transformer architecture and the advantages of being open-source, empowering developers to access the original source code, leading to the ongoing revolution in generative AI. It’s fair to say that BERT paved the way for the generative AI revolution we are witnessing these days.
Features of BERT
- Bidirectional Contextual Understanding: BERT revolutionized NLP with its bidirectional contextual understanding, capturing dependencies and relationships between words in both directions to achieve deep contextual understanding.
- Transfer Learning: It enables transfer learning across tasks and domains, leveraging pre-trained representations to improve performance on downstream tasks with limited labeled data.
- Fine-Grained Embeddings: BERT generates fine-grained word embeddings, capturing rich semantic information and contextual nuances to enhance language understanding and representation learning.
Comparison of popular LLM Models
| Model/Model Family Name | Created By | Sizes | Versions | Pretraining Data | Fine-tuning and Alignment Details | License | What’s Interesting | Architectural Notes |
|---|---|---|---|---|---|---|---|---|
| GPT-4 | OpenAI | Not specified (rumored to have >170 trillion parameters) | Not specified | Not specified | Reinforcement Learning from Human Feedback, adversarial testing | Not specified | Multimodal, excels in complex reasoning, advanced coding | First multimodal model, improved factuality |
| GPT-3 | OpenAI | Various (e.g., GPT-3, GPT-3.5) | Multiple | Large-scale text corpora | Not specified | Open-source | Record-breaking 175 billion parameters, revolutionized NLP | Decoder-only transformer architecture |
| GPT-3.5 | OpenAI | Not specified | Not specified | Large-scale text corpora | Reinforcement learning from human feedback | Open-source | Reduced parameter count, serves as underlying technology for ChatGPT | Offers GPT-3.5 turbo, fast inference |
| Gemini | Not specified | Not specified | Not specified | Fine-tuned on various datasets | Not specified | Outperforms ChatGPT in understanding text, images, videos, speech | Multimodal, excels in academic tests | |
| LLaMA | Meta AI | Various (e.g., LLaMA-7B, LLaMA-65B) | Not specified | Not specified | Not specified | Open-source | Diverse range of models, superior performance compared to GPT-3 | Empowers developers with open-source models |
| PaLM 2 (Bison-001) | Google AI | Up to 540 billion parameters | Not specified | Large-scale text corpora | Multilingual proficiency, comprehension of idioms | Not specified | Advanced proficiency in formal logic, mathematical equations | Multilingual, quick response |
| Bard | Google AI | 1.6 trillion parameters | Not specified | Not specified | Tailored for natural conversations, internet-connected | Not specified | Real-time access to online information, tailored for dialogue | Internet-connected, tailored for conversations |
| Claude v1 | Anthropic | Not specified | Not specified | Not specified | Not specified | Not specified | Outperforms PaLM 2 in benchmark tests, offers 100k token context window | Competing with GPT-4, superior performance |
| Falcon | Technology Innovation Institute(TII), UAE | Not specified | Not specified | Web text, curated sources | Incorporates enhancements like rotary positional embeddings | Open-source | Outranks other open-source models, improved performance | Trained on extensive dataset, multi-query attention |
| Cohere | Cohere | Various (e.g., 6B, 52B) | Not specified | Not specified | Custom-trained and fine-tuned to specific company’s use case | Commercial | Customizable for enterprise applications | Custom-trained and fine-tuned models |
| Orca | Microsoft | 13 billion parameters | Not specified | Not specified | Synthetic training dataset, Prompt Erasure technique | Not specified | Comparable performance to GPT-4, efficient on laptops | Fine-tuned version of LLaMA 2, uses synthetic data |
| Guanaco | Not specified | Various (e.g., Guanaco-7B, Guanaco-65B) | Not specified | OASST1 dataset | QLoRA fine-tuning technique | Not specified | Surpasses GPT-3.5 in performance, optimized memory usage | Trained on OASST1 dataset, QLoRA technique |
| Vicuna | LMSYS | Not specified | Not specified | User-shared ChatGPT conversations | Trained on a budget, high performance for its size | Not specified | Efficient training process, competitive performance | Trained on user-shared conversations |
| MPT-30B | Not specified | Not specified | Not specified | Various datasets | Long context lengths, exceeds quality of GPT-3 | Apache 2.0 | Various model configurations, optimized for specific requirements | Fine-tuned on massive corpus of data |
| 30B Lazarus | CalderaAI | Not specified | Not specified | LoRA-tuned datasets | Exceptional performance, top open-source model for text generation | Not specified | Excels in text generation, supports specific use cases | Utilizes LoRA-tuned datasets, specific use cases |
| Flan-T5 | Google researchers | Various (e.g., Flan-T5-Large) | Not specified | Supervised, unsupervised datasets | Supports various language tasks, text-to-text paradigm | Open-source | Supports multiple language tasks, detects “toxic” language | Encoder-decoder model, text-to-text paradigm |
| WizardLM | Not specified | Not specified | Not specified | Evol-instruct approach | Impressive performance despite 13B parameters | Open-source | Efficient and compact, excels in executing complex instructions | Utilizes Evol-instruct approach for fine-tuning |
| Alpaca 7B | Stanford University | 7 billion parameters | Not specified | Not specified | Cost-effective creation, quantitative comparison to text-davinci-003 | Not specified | Cost-effective, comparable performance to text-davinci-003 | Utilizes mixed precision, Fully Sharded Data Parallel training |
| LaMDA | Not specified | Not specified | Billions of documents, dialogs, utterances | Crafted responses, access to symbolic text processing systems | Not specified | Versatile, access to multiple symbolic text processing systems | Relies on powerful Transformer architecture | |
| BERT | Not specified | Not specified | Large-scale text corpora | Standard in NLP tasks, open-source | Open-source | Pioneering model in NLP, standard for language understanding | Transformer architecture, open-source |
Conclusion
In essense, the exploration of the top 20 LLMs provides a glimpse into the current state of the art and the potential avenues for future advancements. These models becomes more impactful, influencing industries