Wichtiger Hinweis: Vorsicht vor Fake-Accounts!
Es gibt Hinweise darauf, dass Fake-Accounts versuchen könnten, dieses Projekt falsch darzustellen. Bitte geben Sie keine persönlichen Daten an Unbekannte weiter und verlassen Sie sich nur auf Inhalte, die direkt aus diesem Repository stammen. Melden Sie umfangreiche Aktivitäten oder Accounts umgehend an GitHub oder das Projektteam.

Lernen Sie MAVIS kennen – Ihren kostenlosen Open-Source-Mathematik-Assistenten, der zum Nutzen aller entwickelt wurde.

MAVIS (Math Visual Intelligent System) ist ein leistungsstarker KI-gestützter Assistent, der komplexe mathematische Probleme lösen, präzise Visualisierungen erstellen und abstrakte Konzepte verständlich erklären kann. Er unterstützt die Analyse verschiedener Eingabeformate – darunter Bilder (PNG, JPG, JPEG, GIF) und Text-/Code-Dokumente (TXT, PDF, PY, DOC, DOCX) – und liefert interaktive, aufschlussreiche Antworten in Echtzeit.

MAVIS wurde mit einem Ziel entwickelt: fortschrittliche mathematische und wissenschaftliche Werkzeuge für alle zugänglich zu machen – ohne Paywalls, ohne Einschränkungen. Es ist völlig kostenlos, offen für Beiträge und wird zum Wohl der Menschheit entwickelt.

Um MAVIS optimal zu nutzen, beachten Sie:

• Verwenden Sie immer LaTeX, um Formeln klar und professionell zu formatieren.
• Der gesamte Code sollte in Python geschrieben sein, der einzigen unterstützten Sprache in der aktuellen Benutzeroberfläche.
• Für Visualisierungen verwenden Sie bitte MAVIS: Matplotlib, Plotly, Seaborn, Bokeh, Mayavi, Manim oder Altair – die Benutzeroberfläche unterstützt die Darstellung über HTML, LaTeX und Python 3.12. Zu den verfügbaren wissenschaftlichen Bibliotheken gehören: NumPy, Math, SymPy, Pandas, AstroPy, QuantLib, OpenMDAO, pybullet, MONAI (und bald: PyTorch, TensorFlow, Keras, Scikit-Learn, Hugging Face Transformers, JAX und mehr).

Nutzen Sie MAVIS, um Ihre Projekte zu optimieren – schnellere Ergebnisse, bessere Visualisierungen, tiefere Einblicke. Weil leistungsstarke Wissenstools jedem zur Verfügung stehen sollten.

Use P-Terminal, it is much stronger and was developed by the same developers.

MAVIS Terminal: Harness the power of MAVIS, Python, PIP, Ollama, Git, vLLM, PowerShell, and a vast array of Linux distributions—including Ubuntu, Debian, Arch Linux, Kali Linux, openSUSE, Mint, Fedora, and Red Hat—all within a single terminal on Windows!

Experience effortless and secure installations with the MAVIS 4 Launcher – your central solution to keep everything up to date and optimally protected in a flash!

And more...

And more...

MAVIS (aka Xc++ III) is currently under development and is not fully available in this repository. Cloning the repository will only give you the README file, some images and already released code, including the user interface (UI) compatible with Qwen 2.5 Code and Llama 3.2 Vision.

Note: Although manual installation is not much more complicated, it is significantly more stable and secure, so we recommend preferring this method.

The automatic installation installs Git, Python and Ollama, creates a folder and sets up a virtual Python environment. It also installs the required Python frameworks and AI models - but this step is also done automatically with the manual installation.

chmod +x mavis-installer.sh

./mavis-installer.sh

MAVIS (alias Xc++ III) befindet sich derzeit in der Entwicklung und ist in diesem Repository nicht vollständig verfügbar. Durch das Klonen des Repositories erhalten Sie lediglich die README-Datei, einige Bilder und bereits veröffentlichte Codes, einschließlich der Benutzeroberfläche (UI), die mit Qwen 2.5 Code und Llama 3.2 Vision kompatibel ist.

Hinweis: Die manuelle Installation ist zwar nicht wesentlich komplizierter, jedoch erheblich stabiler und sicherer. Deshalb empfehlen wir, diese Methode zu bevorzugen.

Bei der automatischen Installation werden Git, Python und Ollama installiert, ein Ordner erstellt und eine virtuelle Python-Umgebung eingerichtet. Außerdem werden die erforderlichen Python-Frameworks und KI-Modelle installiert – dieser Schritt erfolgt jedoch auch bei der manuellen Installation automatisch.

Demo 11

Demo 10

Aufgabe: Du bist ein Professioneller Thermodynamik Lehrer. Fasse mir die Übersicht zusammen in einer Tabelle (Formelsammlung) und lege dich ins Zeug!

Grafik: https://www.ulrich-rapp.de/stoff/thermodynamik/Gasgesetz_AB.pdf

Demo 9

Task: In this task, students are to create a phase diagram for a binary mixed system that shows the phase transitions between the liquid and vapor phases. They use thermodynamic models and learn how to visualize complex phase diagrams using Python and the Matplotlib and Seaborn libraries.

Use the Antoine equation to calculate the vapor pressure for each of the two components at a given temperature. The vapor pressure formula is:

$$ P_A = P_A^0 \cdot x_A \quad \text{and} \quad P_B = P_B^0 \cdot x_B $$

Where $P_A^0$ and $P_B^0$ are the vapor pressures of the pure components at a given temperature $T$, and $x_A$ and $x_B$ are the mole fractions of the two components in the liquid phase.

Given data: The vapor pressure parameters for the two components A and B at different temperatures are described by the Antoine equation. The corresponding constants for each component are:

For component A:

• $A_A = 8.07131$

• $B_A = 1730.63$

• $C_A = 233.426$

For component B:

• $A_B = 8.14019$

• $B_B = 1810.94$

• $C_B = 244.485 $

Good luck!

Demo 8

Demo 7

Demo 6

Demo 5

Demo 4

Demo 3

Demo 2

Demo 1

Authors: Yukun Zhu, Ryan Kiros, Richard Zemel ...
Link: arXiv:1506.06724
Abstract: This work presents a Neural Architecture Search (NAS) method using reinforcement learning to automatically generate neural network architectures. NAS demonstrates the ability to discover novel architectures that outperform human-designed models on standard benchmarks.

Authors: Tom B. Brown, Benjamin Mann, Nick Ryder ...
Link: arXiv:2005.14165v4
Abstract: Recent work has demonstrated substantial gains on many NLP tasks and benchmarks by pre-training on a large corpus of text followed by fine-tuning on a specific task. While typically task-agnostic in architecture, this method still requires task-specific fine-tuning datasets of thousands or tens of thousands of examples. By contrast, humans can generally perform a new language task from only a few examples or from simple instructions - something which current NLP systems still largely struggle to do. Here we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches. Specifically, we train GPT-3, an autoregressive language model with 175 billion parameters, 10x more than any previous non-sparse language model, and test its performance in the few-shot setting. For all tasks, GPT-3 is applied without any gradient updates or fine-tuning, with tasks and few-shot demonstrations specified purely via text interaction with the model. GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic. At the same time, we also identify some datasets where GPT-3's few-shot learning still struggles, as well as some datasets where GPT-3 faces methodological issues related to training on large web corpora. Finally, we find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans. We discuss broader societal impacts of this finding and of GPT-3 in general.

Authors: Chenfei Wu, Shengming Yin, Weizhen Qi ...
Link: arXiv:2303.04671
Abstract: ChatGPT is attracting a cross-field interest as it provides a language interface with remarkable conversational competency and reasoning capabilities across many domains. However, since ChatGPT is trained with languages, it is currently not capable of processing or generating images from the visual world. At the same time, Visual Foundation Models, such as Visual Transformers or Stable Diffusion, although showing great visual understanding and generation capabilities, they are only experts on specific tasks with one-round fixed inputs and outputs. To this end, We build a system called \textbf{Visual ChatGPT}, incorporating different Visual Foundation Models, to enable the user to interact with ChatGPT by 1) sending and receiving not only languages but also images 2) providing complex visual questions or visual editing instructions that require the collaboration of multiple AI models with multi-steps. 3) providing feedback and asking for corrected results. We design a series of prompts to inject the visual model information into ChatGPT, considering models of multiple inputs/outputs and models that require visual feedback. Experiments show that Visual ChatGPT opens the door to investigating the visual roles of ChatGPT with the help of Visual Foundation Models.

Authors: Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, Russ Altman ...
Link: arXiv:2108.07258
Abstract: AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e.g., model architectures, training procedures, data, systems, security, evaluation, theory) to their applications (e.g., law, healthcare, education) and societal impact (e.g., inequity, misuse, economic and environmental impact, legal and ethical considerations). Though foundation models are based on standard deep learning and transfer learning, their scale results in new emergent capabilities,and their effectiveness across so many tasks incentivizes homogenization. Homogenization provides powerful leverage but demands caution, as the defects of the foundation model are inherited by all the adapted models downstream. Despite the impending widespread deployment of foundation models, we currently lack a clear understanding of how they work, when they fail, and what they are even capable of due to their emergent properties. To tackle these questions, we believe much of the critical research on foundation models will require deep interdisciplinary collaboration commensurate with their fundamentally sociotechnical nature.

Authors: Long Ouyang, Jeff Wu, Xu Jiang ...
Link: arXiv:2203.02155
Abstract: Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine-tune GPT-3 using supervised learning. We then collect a dataset of rankings of model outputs, which we use to further fine-tune this supervised model using reinforcement learning from human feedback. We call the resulting models InstructGPT. In human evaluations on our prompt distribution, outputs from the 1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3, despite having 100x fewer parameters. Moreover, InstructGPT models show improvements in truthfulness and reductions in toxic output generation while having minimal performance regressions on public NLP datasets. Even though InstructGPT still makes simple mistakes, our results show that fine-tuning with human feedback is a promising direction for aligning language models with human intent.

Authors: Renqian Luo, Liai Sun, Yingce Xia ...
Link: arXiv:2210.10341
Abstract: Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e., BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large scale biomedical literature. We evaluate BioGPT on six biomedical NLP tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.

Authors: Irene Solaiman, Miles Brundage, Jack Clark ...
Link: arXiv:1908.09203
Abstract: Large language models have a range of beneficial uses: they can assist in prose, poetry, and programming; analyze dataset biases; and more. However, their flexibility and generative capabilities also raise misuse concerns. This report discusses OpenAI's work related to the release of its GPT-2 language model. It discusses staged release, which allows time between model releases to conduct risk and benefit analyses as model sizes increased. It also discusses ongoing partnership-based research and provides recommendations for better coordination and responsible publication in AI.

**Authors:**Reiichiro Nakano, Jacob Hilton, Suchir Balaji ...
Link: arXiv:2112.09332
Abstract: We fine-tune GPT-3 to answer long-form questions using a text-based web-browsing environment, which allows the model to search and navigate the web. By setting up the task so that it can be performed by humans, we are able to train models on the task using imitation learning, and then optimize answer quality with human feedback. To make human evaluation of factual accuracy easier, models must collect references while browsing in support of their answers. We train and evaluate our models on ELI5, a dataset of questions asked by Reddit users. Our best model is obtained by fine-tuning GPT-3 using behavior cloning, and then performing rejection sampling against a reward model trained to predict human preferences. This model's answers are preferred by humans 56% of the time to those of our human demonstrators, and 69% of the time to the highest-voted answer from Reddit.

Authors: OpenAI, Josh Achiam, Steven Adler ...
Link: arXiv:2303.08774
Abstract: We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4.

Authors: Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan ...
Link: arXiv:2303.12712
Abstract: Artificial intelligence (AI) researchers have been developing and refining large language models (LLMs) that exhibit remarkable capabilities across a variety of domains and tasks, challenging our understanding of learning and cognition. The latest model developed by OpenAI, GPT-4, was trained using an unprecedented scale of compute and data. In this paper, we report on our investigation of an early version of GPT-4, when it was still in active development by OpenAI. We contend that (this early version of) GPT-4 is part of a new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that exhibit more general intelligence than previous AI models. We discuss the rising capabilities and implications of these models. We demonstrate that, beyond its mastery of language, GPT-4 can solve novel and difficult tasks that span mathematics, coding, vision, medicine, law, psychology and more, without needing any special prompting. Moreover, in all of these tasks, GPT-4's performance is strikingly close to human-level performance, and often vastly surpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's capabilities, we believe that it could reasonably be viewed as an early (yet still incomplete) version of an artificial general intelligence (AGI) system. In our exploration of GPT-4, we put special emphasis on discovering its limitations, and we discuss the challenges ahead for advancing towards deeper and more comprehensive versions of AGI, including the possible need for pursuing a new paradigm that moves beyond next-word prediction. We conclude with reflections on societal influences of the recent technological leap and future research directions.

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