GitHub - kaistAI/CoT-Collection: [Under Review] The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
Innovations like prompting, chain-of-thought reasoning, retrieval grounding, and others are needed to educate models.
Ben Auffarth • Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs
uniflow provides a unified LLM interface to extract and transform and raw documents.
- Document types: Uniflow enables data extraction from PDFs, HTMLs and TXTs.
- LLM agnostic: Uniflow supports most common-used LLMs for text tranformation, including
- OpenAI models (GPT3.5 and GPT4),
- Google Gemini models (Gemini 1.5, MultiModal),
- AWS BedRock models,
- Huggingf
CambioML • GitHub - CambioML/uniflow-llm-based-pdf-extraction-text-cleaning-data-clustering: LLM-based text extraction from unstructured data like PDFs, Words and HTMLs. Transform and cluster the text into your desired format. Less information loss, more interpretation, and faster...
GitHub - llmkit-ai/llmkit: A prompt management, versioning, testing, and evaluation inference server and UI toolkit. Provider agnostic and OpenAI API compatible.
Dangithub.com
LLM-PowerHouse: A Curated Guide for Large Language Models with Custom Training and Inferencing
Welcome to LLM-PowerHouse, your ultimate resource for unleashing the full potential of Large Language Models (LLMs) with custom training and inferencing. This GitHub repository is a comprehensive and curated guide designed to empower developers, researche... See more
Welcome to LLM-PowerHouse, your ultimate resource for unleashing the full potential of Large Language Models (LLMs) with custom training and inferencing. This GitHub repository is a comprehensive and curated guide designed to empower developers, researche... See more
ghimiresunil • GitHub - ghimiresunil/LLM-PowerHouse-A-Curated-Guide-for-Large-Language-Models-with-Custom-Training-and-Inferencing: LLM-PowerHouse: Unleash LLMs' potential through curated tutorials, best practices, and ready-to-use code for custom training and inferencing.
Text embeddings are a critical piece of many pipelines, from search, to RAG, to vector databases and more. Most embedding models are BERT/Transformer-based and typically have short context lengths (e.g., 512). That’s only about two pages of text, but documents can be very long – books, legal cases, TV screenplays, code repositories, etc can be tens... See more
Long-Context Retrieval Models with Monarch Mixer
Best 100+ Stable Diffusion Prompts: The Most Beautiful AI Text-to-Image Prompts
Metaverse Postmpost.io