#LLM #data #linguistics A [[Large Language Model|large language model]], such as GPT-3.5, is a powerful [[Hub/Theory/Sciences/Computer Science/AI|artificial intelligence]] system designed to understand and generate human-like text. These models are built using deep learning techniques, particularly transformers, which enable them to capture and learn complex patterns in language data. A large language model is a type of artificial intelligence ([[AI]]) model that is designed to understand and generate human-like text. These models are built using deep learning techniques and are trained on vast amounts of text data from various sources such as books, articles, and websites. Large language models have the ability to process and understand natural language, allowing them to generate coherent and contextually relevant responses. They can be used for a wide range of applications such as chatbots, virtual assistants, content generation, translation services, sentiment analysis, and more. The size of a large language model refers to the number of parameters it has. Models with billions of parameters are increasingly common in recent years. Examples of prominent large language models include OpenAI's GPT (Generative Pre-trained Transformer) series, Google's BERT (Bidirectional Encoder Representations from Transformers), and Facebook's RoBERTa (Robustly Optimized BERT approach). These models have demonstrated impressive capabilities in understanding and generating human-like text. # A Field Theory-based LLM model See [[Knowledge representation in Field Theory and CCA]] ## Compositionality at the Linguistic Level The [[Compositionality|compositionality]] of data refers to the ability to understand and generate meaningful combinations of smaller linguistic units to form larger, more complex expressions. Language models play a crucial role in understanding and leveraging compositionality in data. They learn to associate individual words or tokens with their semantic meaning, and they also learn to recognize and generate coherent combinations of words that form meaningful phrases, sentences, and even longer text passages. For example, if a language model is trained on a large corpus of text that includes the phrase "The cat sat on the mat," it learns that "cat," "sat," "on," and "mat" can be combined to create a coherent sentence. It then generalizes this knowledge to generate similar sentences when prompted with related inputs. By capturing the statistical regularities in language data, large language models excel at understanding and generating compositions of words, phrases, and sentences. They can go beyond simple word associations and grasp the syntactic and semantic relationships between different linguistic units. This enables them to compose text that is coherent and contextually appropriate. The compositionality of data is vital in various natural language processing tasks, such as machine translation, question answering, text summarization, and dialogue systems. Language models help in these tasks by effectively capturing the compositional structure of language, allowing them to generate coherent and contextually relevant responses. ## Advancement in Compositionality and in LLM The creation of tools such as [[Literature/PKM/Tools/Open Source/Langchain]] is a critical step in streamlining the emergence of data assets through the composition of data processing workflows. When [[Literature/PKM/Tools/Open Source/Langchain]] is integrated with [[Zettlekasten Workflow]]/[[Intentional Workflow|intentional workflow]] and supported by a fluid frontend tool such [[Obsidian]], a new level of productivity could [[Emergence|emerge]]. ## Where are LLM data stored? Here's a breakdown of directories on Ubuntu where you might find large language model (LLM) files, along with factors that can influence their location: **Typical Locations:** - **~/.cache/torch/hub:** The default location for models downloaded using PyTorch's `torch.hub` functionality. - **~/.cache/transformers:** Models downloaded using the popular Hugging Face Transformers library often reside here. - **~/.keras/models:** Keras (a deep learning framework) might store models in this directory. - **Project-Specific Directories:** If your LLM is part of a project, it's likely within the main project directory or a subdirectory created for models. **Factors Influencing Location:** - **Installation Method:** - **PyTorch's `torch.hub`:** Stores models in `~/.cache/torch/hub`. - **Hugging Face Transformers:** Leverages `~/.cache/transformers`. - **Manual Download:** The location depends on where you saved the model files. - **Custom Environment Variables:** The `TORCH_HOME` (for PyTorch) or other framework-specific environment variables can change the default model storage location. - **User Preferences:** Some applications might let you choose where to save model files. **How to Find Large Language Model Files** 1. **Search Tools:** - **`find / -type f -size +100M`** - Searches from the root directory (/) for files larger than 100MB. Adjust the size as needed. - **`du -h / | sort -h`** - Shows disk usage per directory, sorted by size, helping to narrow down potential locations. 2. **Check Likely Directories:** Manually inspect the common directories mentioned earlier. 3. **Consider Your Workflow:** Think about where you typically download or save large model files in your projects. # References ```dataview Table title as Title, authors as Authors where contains(subject, "LLM") ```