This paper aims to understand how neural networks work by breaking them down into smaller components that are easier to comprehend. However, it turns out that individual neurons are not easy to understand because they respond to a mix of unrelated inputs. One reason for this is superposition, where a neural network represents more features than it has neurons. The paper explores different approaches to finding interpretable features hidden by superposition, such as sparse autoencoders. By using sparse autoencoders, the researchers were able to extract meaningful features from a neural network and analyze its behavior. The paper provides detailed investigations, global analyses, and visualizations to support their findings.
ToolLLM is a project that aims to create a large-scale dataset for training language models with tool-use capabilities. They collect instructions involving real-world APIs and develop a new annotation approach to improve efficiency. The project provides the ToolLLaMA model, which performs well in handling single-tool and complex multi-tool instructions. They also release the ToolLLaMA-7b, ToolLLaMA-7b-LoRA, and ToolLLaMA-2-7b models, along with a tool retriever. They evaluate the models using pass rate and preference metrics, showing good performance compared to other models. Overall, ToolLLM empowers language models to understand and use real-world tools effectively.
Cheetor, a Transformer-based multi-modal large language model equipped with controllable knowledge re-injection. Cheetor demonstrates strong capabilities in reasoning over complicated interleaved vision-language instructions. It can identify connections between images, infer causes and reasons, understand metaphorical implications, and comprehend absurd objects through multi-modal conversations with humans.
The gpt-prompt-engineer tool is a powerful solution for prompt engineering, enabling users to experiment and find the optimal prompt for GPT-4 and GPT-3.5-Turbo language models. It generates a variety of prompts based on the provided use-case and test cases, and then tests and ranks them using an ELO rating system. Additionally, there is a specific classification version that evaluates test case correctness and provides scores for each prompt. The tool also supports optional logging to Weights & Biases, allowing for tracking of configurations and prompt performance.
WizardCoder is a new model that utilizes complex instruction fine-tuning to enhance Code Large Language Models (Code LLMs) for code-related tasks. This model has been tested on four benchmarks and has shown superior performance compared to other Code LLMs and even outperforms the largest closed LLMs on some benchmarks.
OlaGPT is a newly developed framework that enhances large language models by simulating human-like problem-solving abilities. It incorporates six cognitive modules, including attention, memory, reasoning, learning, decision-making, and action selection. The model was evaluated on algebraic word problems and analogical reasoning questions, showing superior performance against existing benchmarks. OlaGPT is integrated with pre-existing models such as GPT-3 as base models, with different cognitive modules added. Although the framework has limitations that prevent it from providing a creative solution, it is a promising tool that could approximate the human brain model.
The Open LLM Leaderboard tracks, ranks, and evaluates language models and chatbots based on various benchmarks. Anyone from the community can submit a model for automated evaluation on the GPU cluster, as long as it is a Transformers model with weights on the Hub. The leaderboard evaluates models on four benchmarks, including AI2 Reasoning Challenge, HellaSwag, MMLU, and TruthfulQA, to test reasoning and general knowledge in both zero-shot and few-shot settings.
QLoRA allows fine-tuning of large language models on a single GPU. Using this method, they trained Guanaco, a family of chatbots based on Meta's LLaMA models, achieving over 99% of ChatGPT's performance. QLoRA reduces the memory requirement by quantizing models to 4 bits and adding low-rank adaptive weights. The team found that data quality is more important than quantity for fine-tuning, with models trained on OpenAssistant data performing better. Even the smallest Guanaco model outperformed other models, and the team believes that QLoRA will make fine-tuning more accessible, bridging the resource gap between large corporations and small teams. They also see potential for private models on mobile devices, enabling privacy-preserving fine-tuning on smartphones.
Tree-of-Thought (ToT) aims to enhance the problem-solving capabilities of large language models (LLMs) like GPT-4. The framework utilizes a deliberate 'System 2' tree search approach to tackle complex and general problems that LLMs struggle with. The author demonstrates significant improvements on three tasks: the game of 24, creative writing, and crosswords, which GPT-4 and CoT (chain of thought, another approach) find challenging due to the need for planning and searching. The limitations of token-by-token decoding, which lacks lookahead, backtrack, and global exploration, are highlighted as the reason for these difficulties. ToT achieves a tenfold performance boost by leveraging the LLM's ability to generate diverse intermediate thoughts, self-evaluate them through deliberate reasoning, and employ search algorithms like breadth-first search (bfs) or depth-first search (dfs) to systematically explore the problem space.
OP Vault is a versatile platform that allows users to upload various document types through a simple react frontend, enabling the creation of a customized knowledge base. It leverages advanced algorithms to provide accurate and relevant answers based on the content of the uploaded documents. Users can gain insights into the answers by viewing filenames and specific context snippets. The user-friendly interface of OP Vault makes it easy to explore the capabilities of the OP Stack, a powerful combination of OpenAI and Pinecone Vector Database. Moreover, OP Vault supports large-scale uploads, making it possible to load entire libraries' worth of books, thus expanding the scope of knowledge accessible through the platform. To ensure smooth operation, certain manual dependencies such as node (v19), go (v1.18.9 darwin/arm64), and poppler are required. With its diverse features, OP Vault offers a convenient solution for document upload, accurate retrieval of answers, and efficient exploration of information.
The paper focuses on a vision-language model called InstructBLIP and explores the process of instruction tuning. The authors collect 26 datasets and categorize them for instruction tuning and zero-shot evaluation. They also introduce a method called instruction-aware visual feature extraction. The results show that InstructBLIP achieves the best performance among all models, surpassing BLIP-2 and Flamingo. When fine-tuned for specific tasks, InstructBLIP exhibits exceptional accuracy, such as 90.7% on the ScienceQA IMG task. Through qualitative comparisons, the study highlights InstructBLIP's superiority over other multimodal models, demonstrating its importance in the field of vision-language tasks.
Inpaint Anything is an innovative tool that seamlessly inpaints images, videos, and 3D scenes by allowing users to remove, fill, or replace objects with just a few clicks. It leverages advanced vision models like Segment Anything Model (SAM), LaMa, and Stable Diffusion (SD) to achieve these tasks. With support for multiple aspect ratios and resolutions up to 2K, Inpaint Anything offers a user-friendly interface for various modalities, including images, videos, and 3D scenes. The tool is continuously improving with new features and functionalities, making it an accessible and powerful solution for users seeking advanced inpainting capabilities.
WizardLM is a pre-trained language model that can follow complex instructions using Evol-Instruct - a method that uses language models instead of humans to automatically produce open-domain instructions of various difficulty levels. WizardLM is still in development and will continue to improve by training on larger scales, adding more training data, and innovating more advanced large-model training methods. To fine-tune WizardLM model, alpaca_evol_instruct_70k.json containing 70K instruction-following data generated from Evol-Instruct was used. In terms of human evaluation, WizardLM achieved significantly better results than Alpaca and Vicuna-7b models on diverse user-oriented instructions including difficult coding generation, debugging, math, reasoning, complex formats, academic writing, and extensive disciplines. Additionally, in the high-difficulty section of the human evaluation test set, WizardLM even outperforms ChatGPT, indicating its significant potential to handle complex instructions.
Hugging Face, a company and AI community that provides free open source tools for machine learning and AI apps, has released HuggingChat, an open source ChatGPT clone that is available for anyone to use or download. The app is based on the Open Assistant Conversational AI Model by Large-scale Artificial Intelligence Open Network (LAION), a global non-profit organization dedicated to democratizing ML research and its applications. HuggingChat was trained with the OpenAssistant Conversations Dataset (OASST1), which was collected up to April 12, 2023, and used reinforcement learning from human feedback methodology to create a high quality human-annotated dataset. The dataset is the product of a worldwide crowdsourcing effort by over 13,000 volunteers.
LocalAI is an API that can be used as a replacement for OpenAI, which supports various models and can run on consumer-grade hardware. It supports ggml compatible models such as LLaMA, alpaca, gpt4all, vicuna, koala, gpt4all-j, cerebras. It uses C bindings for faster inference and performance and comes as a container image that can be run with docker-compose. The API can be used for running text generation as a service, following the OpenAI reference.
MiniChain is a library used to link prompts together in a sequence, with the ability to manipulate and visualize them using Gradio. Users can ensure the prompt output matches specific criteria through the use of data classes and typed prompts. The library does not manage documents or provide tools, but suggests using the Hugging Face Datasets library for that purpose. Additionally, users can include their own backends.
The AutoGPT website offers an AI buddy that can be set up with initial roles and goals without the requirement of human supervision. This AI tool automatically leverages all available resources to achieve your set goal. The tool is inspired by Auto-GPT and features internet access for information gathering and searches. It also allows users to save chat history, credentials, and definition of AI directly in the browser.
The paper explores the potential of developing autonomous cooperation for conversational language models without relying heavily on human input. The proposed framework named role-playing utilizes inception prompting to direct chat agents toward tasks that align with human intentions while enhancing consistency. The role-playing framework produces conversational data for investigating the behaviors and capabilities of language models, resulting in a valuable resource for studying conversational language models. The authors' contributions include the introduction of a novel communicative agent framework, offering a scalable approach for investigating multi-agent systems, and making the library available for further research on communicative agents.
The article presents a novel approach to large multimodal language models using machine-generated instruction-following data, which has shown promise in improving zero-shot capabilities on tasks in the language domain. The authors introduce LLaVA (Large Language-and-Vision Assistant), an end-to-end trained multimodal model that combines a vision encoder and LLM for general-purpose visual and language understanding. The model demonstrated impressive multimodal chat abilities in early experiments and yields an 85.1% relative score compared with GPT-4 on a synthetic multimodal instruction-following dataset. When fine-tuned on Science QA, LLaVA and GPT-4 achieved a new state-of-the-art accuracy of 92.53%.
MiniGPT-4, a vision-language model that aligns a frozen visual encoder with a frozen large language model (LLM) using one projection layer. The authors trained MiniGPT-4 using a two-stage process, with the first stage using 5 million aligned image-text pairs for traditional pretraining. To address generation issues, they proposed a novel approach using a small, high-quality dataset and ChatGPT to create high-quality image-text pairs. The second stage involved finetuning the model on this dataset using a conversation template to improve generation reliability and overall usability. The results show that MiniGPT-4 processes capabilities similar to GPT-4, such as detailed image description generation and website creation from handwritten drafts, as well as other emerging capabilities, like writing stories and poems based on images and teaching users how to cook with food photos. The method is computationally efficient and highlights the potential of advanced large language models for vision-language understanding.
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