#cplusplus #4_bits #attention_sink #chatbot #chatpdf #intel_optimized_llamacpp #large_language_model #llm_cpu #llm_inference #smoothquant #sparsegpt #speculative_decoding #stable_diffusion #streamingllm

https://github.com/intel/intel-extension-for-transformers

#rust #agent #ai #artificial_intelligence #automation #generative_ai #large_language_model #llm #llmops #rust #scalable_ai

Rig is a Rust library that helps you build apps using Large Language Models (LLMs) like OpenAI and Cohere. It makes it easy to integrate these models into your application with minimal code. Rig supports various vector stores like MongoDB and Neo4j, and it provides simple but powerful tools to work with LLMs. To get started, you can add Rig to your project using `cargo add rig-core` and follow the examples provided. This library is constantly improving, so your feedback is valuable. Using Rig can save you time and effort by providing a straightforward way to use LLMs in your projects.

https://github.com/0xPlaygrounds/rig

#python #knowledge_graph #large_language_model #logical_reasoning #multi_hop_question_answering #trustfulness

KAG (Knowledge Augmented Generation) is a powerful tool that helps computers understand and reason with complex information better. It uses large language models and a special engine to build logical reasoning and question-answering systems, especially in professional domains like medicine or finance. KAG improves upon older methods by reducing errors and noise, and it can handle multiple steps of reasoning and fact-checking.

The benefit to the user is that KAG provides more accurate and reliable answers to complex questions, integrating both structured and unstructured data. This makes it very useful for professionals who need precise information and logical reasoning in their work.

https://github.com/OpenSPG/KAG

#python #artificial_intelligence #large_language_model

This project, called **MiniMind**, is about training a very small language model from scratch. Here are the key points MiniMind is extremely lightweight, with the smallest version being only 26.88MB, which is \(\frac{1}{7000}\) the size of GPT-3. This makes it possible to train and infer on ordinary personal GPUs.
- **Quick Training** The project includes code for pretraining, supervised fine-tuning, LoRA fine-tuning, and DPO preference optimization, along with data processing and testing scripts.
- **Compatibility** It serves as a tutorial for beginners to get started with large language models (LLMs) quickly.
- **Multi-Modal Extension**: There is an extension for visual multi-modal capabilities called **MiniMind-V**.

This project helps users train a small but effective language model quickly and efficiently, making it easier for newcomers to enter the field of LLMs.

https://github.com/jingyaogong/minimind