Types of Large Language Models

1. Autoregressive Models

These models predict subsequent words based on previous words in a sentence. They are widely used for tasks like text completion and creative writing. GPT (Generative Pre-trained Transformer) is a prime example of an autoregressive model.

2. Autoencoding Models

Autoencoders are designed to compress and then decompress input data, typically used for tasks like paraphrasing or text summarization where the context needs to be preserved. BERT (Bidirectional Encoder Representations from Transformers) operates under this model by encoding text all at once and then using the encoded forms to produce outputs.

3. Multimodal Models

These LLMs process and generate information across different data types, such as text, images, and sounds. An example is CLIP (Contrastive Language–Image Pre-training), which can understand images and text simultaneously, aiding in tasks that require a holistic understanding of multiple input types

Free Large Language Models

1. GPT-2

• Description: GPT-2, developed by OpenAI, is a smaller, yet highly versatile version of its successors, suitable for a variety of text generation tasks.
• Applications: Writing assistance, chatbots, and more.
• Access Link: OpenAI GPT-2

2. BERT

• Description: Google’s BERT has revolutionized how machines understand human language by interpreting the context of words in sentences.
• Applications: Text classification, sentiment analysis, and question answering.
• Access Link: Google BERT

3. EleutherAI GPT-Neo

• Description: GPT-Neo is an alternative to OpenAI's GPT models, aiming to provide a similar functionality that is entirely open-source.
• Applications: Text generation, educational tools, and research.
• Access Link: EleutherAI GPT-Neo

4. CLIP

• Description: CLIP from OpenAI is a multimodal model capable of understanding and generating information about images and text simultaneously.
• Applications: Image captioning, content creation, and assistive technologies.
• Access Link: OpenAI CLIP

5. RoBERTa (Robustly Optimized BERT Approach)

• Developer: Facebook AI
• Features: An optimized version of BERT with changes in the pre-training procedure, focusing on more robust performance across a wider range of NLP tasks.
• Applications: Text classification, sentiment analysis.
• Access Link: RoBERTa on GitHub

6. T5 (Text-To-Text Transfer Transformer)

• Developer: Google AI
• Features: Treats every language problem as a text-to-text problem, providing a unified framework to handle different tasks.
• Applications: Translation, summarization, question answering.
• Access Link: T5 on GitHub

7. DistilBERT

• Developer: Hugging Face
• Features: A smaller, faster, cheaper, and lighter version of BERT, DistilBERT retains 97% of BERT’s performance while being 40% smaller.
• Applications: Resource-limited applications needing fast processing.
• Access Link: DistilBERT on Hugging Face

8. XLNet

• Developer: Google/CMU
• Features: Outperforms BERT on several NLP benchmarks by using a permutation-based training method.
• Applications: Natural language understanding, more complex text generation.
• Access Link: XLNet on GitHub

9. ALBERT (A Lite BERT)

• Developer: Google Research
• Features: A version of BERT with far fewer parameters, designed to reduce memory consumption and increase training speed.
• Applications: Large-scale implementations, maintaining model scalability.
• Access Link: ALBERT on GitHub

10. DialoGPT

• Developer: Microsoft
• Features: Designed specifically for conversational applications, this model extends the GPT-2 model to dialogue generation.
• Applications: Chatbots, conversational agents.
• Access Link: DialoGPT on GitHub

11. ERNIE (Enhanced Representation through kNowledge Integration)

• Developer: Baidu
• Features: Incorporates knowledge graphs into training to improve language representation.
• Applications: Question answering, named entity recognition.
• Access Link: ERNIE on GitHub

12. BART (Bidirectional and Auto-Regressive Transformers)

• Developer: Facebook AI
• Features: Combines the benefits of BERT and GPT by both conditioning on the left and right context in all layers.
• Applications: Text generation, comprehension, and translation.
• Access Link: BART on GitHub

13. Longformer

• Developer: Allen AI
• Features: Designed for processing long documents by extending the self-attention mechanism to much larger contexts.
• Applications: Document summarization, long-form question answering.
• Access Link: Longformer on GitHub

14. DeBERTa (Decoding-enhanced BERT with disentangled attention)

• Developer: Microsoft
• Features: Improves upon BERT and RoBERTa models by using a disentangled attention mechanism that separates the content and position for better token representations.
• Applications: Natural language understanding and ranking tasks.
• Access Link: DeBERTa on GitHub

15. Megatron-LM

• Developer: NVIDIA
• Features: Designed to efficiently train large-scale language models using model parallelism, Megatron-LM facilitates the training of massive models that wouldn't typically fit into a single GPU memory.
• Applications: Advanced natural language understanding and generation tasks.
• Access Link: Megatron-LM on GitHub

16. BlenderBot

• Developer: Facebook AI
• Features: A large-scale conversational agent designed to blend a diverse set of conversational skills, including empathy, knowledge, and personality, tailored through its model.
• Applications: Conversational agents, social media bots.
• Access Link: BlenderBot on GitHub

17. CTRL (Conditional Transformer Language Model for Controllable Generation)

• Developer: Salesforce
• Features: Trained with control codes that guide the style, content, and task-specific behavior, allowing for controlled generation of text.
• Applications: Text generation where control over style and tone is required.
• Access Link: CTRL on GitHub

18. MobileBERT

• Developer: Google
• Features: A compact, optimized version of BERT for mobile devices, designed to deliver BERT-level performance with significantly lower latency and smaller model size.
• Applications: On-device NLP tasks like text classification and question answering.
• Access Link: MobileBERT on GitHub

19. Reformer

• Developer: Google Research
• Features: Known for handling very long sequences using an efficient self-attention mechanism called the Locality-Sensitive Hashing (LSH) attention, which reduces the complexity and resource requirements.
• Applications: Tasks requiring the processing of very long documents or sequences.
• Access Link: Reformer on GitHub

20. FlauBERT

• Developer: French National Centre for Scientific Research (CNRS) and Sorbonne University
• Features: Tailored to understand and process French language better, it is trained specifically on a wide and diverse range of French texts.
• Applications: French language understanding, translation, and generation tasks.
• Access Link: FlauBERT on GitHub

Conclusion

As we've explored an array of powerful and versatile Large Language Models, it's clear that the field of AI and NLP is evolving rapidly, providing tools that can transform how we interact with technology. These models open up a realm of possibilities for developers, researchers, and businesses alike to innovate and improve their applications.

If you're excited about the potential of these models and want to stay updated on the latest trends, tools, and discussions in AI, consider following me on social media. You can connect with me on Twitter @promptyourjob for quick updates and engaging content, or join our professional network on LinkedIn here for more in-depth articles, discussions, and networking opportunities. Together, let's dive deeper into the world of AI and explore how these technologies can shape the future.

Your engagement and feedback are invaluable. Let's continue this conversation and push the boundaries of what's possible with AI!

Call to Action

Explore these models through their provided links and consider how they might be incorporated into your own projects. Whether you're a student looking to delve into AI, a developer aiming to integrate advanced features into your apps, or just an AI enthusiast curious about the latest technology, these tools provide a valuable resource. Dive in, experiment, and perhaps contribute back to the community to help push the boundaries of what these powerful models can achieve.