Large Language Models (LLMs): Building Your Own AI

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Large Language Models represent a significant leap in artificial intelligence, particularly in natural language processing and generation. These models have transformed how machines understand and generate human-like text.

The concept of LLMs emerged from decades of research in natural language processing. However, the current era of powerful LLMs began with the introduction of the Transformer architecture by Google researchers in 2017.

Key Concepts in LLMs

Transformers

Transformers are the architectural backbone of modern LLMs. Unlike previous sequential models, Transformers process entire sequences of text simultaneously, allowing for more efficient training on vast amounts of data.

Self-Attention

At the heart of Transformers is the self-attention mechanism. This allows the model to weigh the importance of different words in a sentence when processing each word, capturing complex relationships and context within the text.

Machine Learning

LLMs are a product of machine learning, specifically deep learning. They learn patterns from vast amounts of text data, adjusting millions or even billions of parameters to improve their understanding and generation of language.

List Of Open-Source LLMs

Pre-2023 LLMs:

  1. T5 (2019/10) – A versatile text-to-text transformer for various NLP tasks, available in multiple sizes.
  2. RWKV 4 (2021/08) – A language model using an RNN-based architecture with infinite context length.
  3. GPT-NeoX-20B (2022/04) – A 20B parameter autoregressive model, comparable to GPT-3.
  4. YaLM-100B (2022/06) – A large 100B parameter model from Yandex, optimized for various language tasks.
  5. UL2 (2022/10) – An open-source model focusing on unified language learning across multiple domains.
  6. Bloom (2022/11) – A 176B parameter multilingual model with broad language support.

2023 LLMs:

  1. ChatGLM (2023/03) – A 6B parameter model optimized for chat, with custom usage restrictions.
  2. Cerebras-GPT (2023/03) – A compute-efficient GPT model family, scalable up to 13B parameters.
  3. Open Assistant (Pythia family, 2023/03) – A 12B parameter model aimed at democratizing LLM alignment.
  4. Pythia (2023/04) – A suite of models (70M to 12B) designed for analyzing training and scaling in LLMs.
  5. Dolly (2023/04) – Instruction-tuned models (3B, 7B, 12B) offering open-source alternatives to proprietary LLMs.
  6. StableLM-Alpha (2023/04) – A suite of models ranging from 3B to 65B parameters, designed by Stability AI.
  7. FastChat-T5 (2023/04) – A compact, commercially friendly chatbot model at 3B parameters.
  8. DLite (2023/05) – Lightweight LLMs (0.124B to 1.5B) suitable for running on minimal hardware.
  9. h2oGPT (2023/05) – A model family (12B to 20B) designed by H2O.ai for open-source LLMs.
  10. MPT-7B (2023/05) – A 7B parameter model with an 84k context length, suited for various tasks.
  11. RedPajama-INCITE (2023/05) – Models ranging from 3B to 7B parameters with instruction-tuned capabilities.
  12. OpenLLaMA (2023/05) – Open reproduction of Meta’s LLaMA, available in sizes up to 13B parameters.
  13. Falcon (2023/05) – Powerful models (7B to 180B) trained on web data with high performance.
  14. GPT-J-6B (2023/06) – A 6B parameter model similar to GPT-3, optimized for efficiency.
  15. MPT-30B (2023/06) – A 30B parameter model with an 8k context length, designed for high performance.
  16. LLaMA 2 (2023/06) – Meta’s open-source model series (7B to 70B), available under custom licenses.
  17. ChatGLM2 (2023/06) – An improved version of ChatGLM, with up to 128k context length.
  18. XGen-7B (2023/06) – A 7B parameter model optimized for long sequence modeling, with 8k context length.
  19. Jais-13b (2023/08) – A 13B parameter Arabic-centric model with instruction-tuned capabilities.
  20. OpenHermes (2023/09) – An open-source model family (7B to 13B) designed by Nous Research.
  21. Mistral 7B (2023/09) – A 7B parameter model with sliding window capabilities up to 16k context length.
  22. ChatGLM3 (2023/10) – The latest version of ChatGLM, with multiple context length options up to 128k.
  23. Skywork (2023/10) – A 13B parameter model designed for high-performance NLP tasks.
  24. Jais-30b (2023/11) – An extended version of the Jais model, with 30B parameters and 8k context length.
  25. Zephyr (2023/11) – A 7B parameter model designed for various NLP applications.
  26. DeepSeek (2023/11) – A model family (7B to 67B) designed with custom licenses and usage restrictions.
  27. Mistral 7B v0.2 (2023/12) – An updated version of Mistral with a 32k context length.
  28. Mixtral 8x7B v0.1 (2023/12) – A mixture of experts model, totaling 46.7B parameters with a 32k context length.
  29. LLM360 Amber (2023/12) – A transparent open-source model family, with a 6.7B parameter model.
  30. SOLAR (2023/12) – A 10.7B parameter model focused on efficient language processing.
  31. phi-2 (2023/12) – A model with 2.7B parameters designed by Microsoft, focusing on efficient training.

2024 LLMs:

  1. RWKV 5 v2 (2024/01) – An updated RWKV model series with up to 7B parameters and infinite context length.
  2. OLMo (2024/02) – A model series by AI2, with 1B to 7B parameters.
  3. Qwen1.5 (2024/02) – A family of models (7B to 72B) with long context lengths up to 32k.
  4. LWM (2024/02) – A large world model series with context lengths up to 1M, available under the LLaMA 2 license.
  5. Jais-30b v3 (2024/03) – An updated 30B parameter model with 8k context length.
  6. Gemma (2024/02) – A model family (2B to 7B) with context lengths up to 8192, under restrictive licenses.
  7. Grok-1 (2024/03) – A 314B parameter model under the Apache 2.0 license.
  8. Qwen1.5 MoE (2024/03) – A mixture of experts model with 14.3B parameters, offering high efficiency.
  9. Jamba 0.1 (2024/03) – A 52B parameter model using an SSM-transformer architecture.
  10. Qwen1.5 32B (2024/04) – A 32B parameter model, the capstone of the Qwen1.5 series.
  11. Mamba-7B (2024/04) – A 7B parameter model using RNN architecture, designed by Toyota Research Institute.
  12. Mixtral8x22B v0.1 (2024/04) – A mixture of experts model totaling 141B parameters with a 64k context length.
  13. Llama 3 (2024/04) – Meta’s third iteration of LLaMA, with models ranging from 8B to 70B parameters.
  14. Phi-3 Mini (2024/04) – A small to medium model (3.8B to 14B parameters) with context lengths up to 128k.
  15. OpenELM (2024/04) – An efficient language model family, with open training and inference frameworks.
  16. Snowflake Arctic (2024/04) – A high-parameter model (480B) optimized for enterprise AI applications.
  17. Qwen1.5 110B (2024/04) – A 110B parameter model, the first 100B+ model in the Qwen1.5 series.
  18. RWKV 6 v2.1 (2024/05) – The latest RWKV model series with up to 7B parameters and infinite context length.
  19. DeepSeek-V2 (2024/05) – An advanced mixture of experts model with 236B parameters and up to 128k context length.
  20. Fugaku-LLM (2024/05) – A 13B parameter model trained on the Fugaku supercomputer.
  21. Falcon 2 (2024/05) – TII’s updated Falcon model series, with an 11B parameter model and 8192 context length.
  22. Yi-1.5 (2024/05) – A model family (6B to 34B) with context lengths up to 4096.
  23. DeepSeek-V2-Lite (2024/05) – A lighter version of DeepSeek-V2, with a 16B parameter model and 32k context length.
  24. Phi-3 small/medium (2024/05) – New additions to the Phi-3 family, with models ranging from 7B to 14B parameters.

Code-specific LLMs:

  1. SantaCoder (2023/01) – A 1.1B parameter model optimized for code generation.
  2. CodeGen2 (2023/04) – A family of models (1B to 16B) designed for programming and natural languages.
  3. StarCoder (2023/05) – A model family (1.1B to 15B) specialized for code, with 8192 context length.
  4. StarChat Alpha (2023/05) – A 16B parameter model optimized for code-related conversations.
  5. Replit Code (2023/05) – A 2.7B parameter model optimized for code generation, with infinite context length.
  6. CodeT5+ (2023/05) – An updated CodeT5 model, ranging from 0.22B to 16B parameters, focused on code understanding.
  7. XGen-7B (2023/06) – A 7B parameter model trained for long sequence modeling, including code.
  8. CodeGen2.5 (2023/07) – A 7B parameter model optimized for multilingual code generation.
  9. DeciCoder-1B (2023/08) – A 1.1B parameter model designed for efficient and accurate code generation.
  10. Code Llama (2023/08) – A model series (7B to 34B) designed by Meta, optimized for code-related tasks.

For a full list of LLMs available for commercial use, see here.

Tools and Frameworks

HuggingFace

HuggingFace has become a central marketplace hub for language models. It provides:

  • A repository of pre-trained models
  • Tools for fine-tuning models on specific tasks
  • Libraries for easy integration of LLMs into applications

LMSYS Chatbot Arena

Chatbot Arena is an open-source research project developed by members from LMSYS and UC Berkeley SkyLab. It used to benchmark LLMs often before they are released.

  • Test models directly by chatting with them
  • Full industry leaderboard of all LLM manufacturers

PyTorch

PyTorch, developed by Facebook’s AI Research lab, is a popular framework for building and training LLMs. It offers:

  • Dynamic computational graphs, allowing for flexible model architectures
  • Efficient GPU acceleration for faster training
  • A user-friendly interface popular among researchers

TensorFlow

Google’s TensorFlow is another major framework used in LLM development. It provides:

  • A comprehensive ecosystem for machine learning
  • Robust tools for model deployment in production environments
  • TensorFlow Extended (TFX) for managing ML pipelines

Applications of LLMs

LLMs have found applications in various fields:

  • Content Creation: Generating articles, stories, and marketing copy
  • Code Generation: Assisting developers by suggesting or writing code
  • Language Translation: Providing more contextually accurate translations
  • Chatbots and Virtual Assistants: Creating more human-like conversational interfaces

Considerations and Challenges

While powerful, LLMs also present challenges:

  • High computational requirements for training and running large models
  • Potential biases reflected from training data
  • Ethical concerns about AI-generated content and misinformation

How We Work with LLMs

Our team stays at the forefront of LLM technology. We can:

  • Develop custom applications using pre-trained open source LLMs
  • Fine-tune models for specific industry or task requirements
  • Implement LLMs responsibly, considering ethical implications and biases
  • Integrate LLM capabilities into existing software systems

LLMs represent a powerful tool in the AI toolkit. When used thoughtfully, they can enhance human capabilities, automate complex tasks, and open new possibilities in human-computer interaction.