SLM_Survey is a dedicated research project focused on small language models (SLMs), aiming to provide an in-depth understanding and technical assessment of these models through research and measurement. The project covers transformer-based, decoder-only language models with parameter sizes ranging from 100M to 5B. By investigating 59 state-of-the-art open-source SLMs, it analyzes their technological innovations and evaluates their capabilities across multiple domains, including common-sense reasoning, context learning, mathematics, and programming. Additionally, runtime costs such as inference latency and memory usage have been benchmarked. This research is of significant value in advancing the study of SLMs.

Cheating LLM Benchmarks is a research initiative aimed at exploring cheating behaviors in automated language model (LLM) benchmarking by constructing what are known as 'null models.' The project’s experiments have revealed that even simple null models can achieve high win rates in these benchmarks, challenging the validity and reliability of current benchmarking practices. This research is crucial for understanding the limitations of current language models and improving benchmarking methodologies.

Paper Central is a comprehensive and convenient academic platform launched by Hugging Face. It consolidates open-source academic resources from various sources such as arXiv, Hugging Face paper pages, models, datasets, Spaces, GitHub, and conference proceedings, helping researchers and developers quickly access and utilize open-source resources.

Local Knowledge Graph is a Flask-based web application that uses a local Llama language model to process user queries, generate step-by-step reasoning, and visualize the thought process in an interactive knowledge graph format. It can also find and display relevant questions and answers based on semantic similarity. Key advantages of this application include real-time visualization of the reasoning process, dynamic knowledge graph representation, computation and display of the strongest reasoning paths, and related Q&A based on semantic similarity.

SciAgentsDiscovery is a system that automates scientific research using multi-agent systems and large-scale ontological knowledge graphs. By integrating large language models, data retrieval tools, and multi-agent learning systems, it autonomously generates and refines research hypotheses, revealing potential mechanisms, design principles, and unexpected material properties. The system demonstrates its ability to discover interdisciplinary relationships, particularly in the field of bio-inspired materials, surpassing traditional human-driven research methods.

AI-Researcher is based on a research project by Stanford University's Natural Language Processing team, designed to assist in the generation and management of research ideas through artificial intelligence technology. This tool takes natural language input of research topics and outputs a series of project proposals, ranking and filtering them to help researchers quickly find innovative and feasible concepts. It includes modules for relevant paper searches, retrieval-based idea generation, idea deduplication, project proposal generation, proposal ranking, and filtering.

AI Timeline is an open-source project that records significant technological milestones in the field of artificial intelligence. It meticulously chronicles key developments in AI technologies, including text-to-image, text-to-video, and large language models. The project is built with Vue and TypeScript, providing a platform for AI enthusiasts and researchers to explore the history and trends of AI.

IncarnaMind is an open-source project aimed at enabling conversational interactions with personal documents (PDF, TXT) using large language models (LLMs) such as GPT, Claude, and local open-source LLMs. The project enhances query efficiency and improves the accuracy of LLMs through a sliding window chunking mechanism and integrated retrievers. It supports multi-document conversational Q&A, overcoming the limitations of single-document interaction while being compatible with various file formats and LLM models.

SLM_Survey is a dedicated research project focused on small language models (SLMs), aiming to provide an in-depth understanding and technical assessment of these models through research and measurement. The project covers transformer-based, decoder-only language models with parameter sizes ranging from 100M to 5B. By investigating 59 state-of-the-art open-source SLMs, it analyzes their technological innovations and evaluates their capabilities across multiple domains, including common-sense reasoning, context learning, mathematics, and programming. Additionally, runtime costs such as inference latency and memory usage have been benchmarked. This research is of significant value in advancing the study of SLMs.

The State of AI Report 2024 is an annual report produced by AI investor Nathan Benaich and Air Street Capital, aimed at analyzing the most interesting developments in the AI field and sparking informed discussions about the current state of AI and its future impacts. The report covers various dimensions including research, industry, politics, and security, providing predictions and assessments for the future of AI development. This report is an essential resource for understanding the latest advancements and trends in AI, making it highly valuable to researchers, entrepreneurs, policymakers, and the interested public.

Cheating LLM Benchmarks is a research initiative aimed at exploring cheating behaviors in automated language model (LLM) benchmarking by constructing what are known as 'null models.' The project’s experiments have revealed that even simple null models can achieve high win rates in these benchmarks, challenging the validity and reliability of current benchmarking practices. This research is crucial for understanding the limitations of current language models and improving benchmarking methodologies.

Paper Central is a comprehensive and convenient academic platform launched by Hugging Face. It consolidates open-source academic resources from various sources such as arXiv, Hugging Face paper pages, models, datasets, Spaces, GitHub, and conference proceedings, helping researchers and developers quickly access and utilize open-source resources.

Local Knowledge Graph is a Flask-based web application that uses a local Llama language model to process user queries, generate step-by-step reasoning, and visualize the thought process in an interactive knowledge graph format. It can also find and display relevant questions and answers based on semantic similarity. Key advantages of this application include real-time visualization of the reasoning process, dynamic knowledge graph representation, computation and display of the strongest reasoning paths, and related Q&A based on semantic similarity.

SciAgentsDiscovery is a system that automates scientific research using multi-agent systems and large-scale ontological knowledge graphs. By integrating large language models, data retrieval tools, and multi-agent learning systems, it autonomously generates and refines research hypotheses, revealing potential mechanisms, design principles, and unexpected material properties. The system demonstrates its ability to discover interdisciplinary relationships, particularly in the field of bio-inspired materials, surpassing traditional human-driven research methods.

AI-Researcher is based on a research project by Stanford University's Natural Language Processing team, designed to assist in the generation and management of research ideas through artificial intelligence technology. This tool takes natural language input of research topics and outputs a series of project proposals, ranking and filtering them to help researchers quickly find innovative and feasible concepts. It includes modules for relevant paper searches, retrieval-based idea generation, idea deduplication, project proposal generation, proposal ranking, and filtering.

AI Timeline is an open-source project that records significant technological milestones in the field of artificial intelligence. It meticulously chronicles key developments in AI technologies, including text-to-image, text-to-video, and large language models. The project is built with Vue and TypeScript, providing a platform for AI enthusiasts and researchers to explore the history and trends of AI.