/ Модель

Характеристики

Контекст
32 768 токенов
Параметры
7,1 млрд
Размер весов
14,5 ГБ
Лицензия
artistic-2.0
Архитектура
mistral · dense
Опубликована
11 окт 2023

ANIMA-Phi-Neptune-Mistral-7B

Генерация текстаТекст → текст3 профессии

ANIMA — дообученная модель Mistral для научных вопросов и поиска решений инженерных задач по принципам биомимикрии.

119скачиваний за 30 дней30лайковвеса обновлены18 мар 2024

Бенчмарки

Как модель справилась с задачами пользователей ailist и что о ней публикуют открытые лидерборды

Тесты пользователейпока нет

Никто ещё не опубликовал тест этой модели. Соберите бенчмарк из своих задач — результат появится здесь.

  1. Добавьте модель в бенчмарк

    Вместе с другими моделями категории «Текст» — сравнение идёт внутри одной категории.

  2. Опишите свои задачи

    Напишите запросы, при необходимости приложите файлы и эталонные ответы.

  3. Запустите и опубликуйте

    Модели ответят на каждую задачу, судья оценит ответы. Опубликованный результат появится на этой странице.

Публичные оценки

Из открытых лидербордов. Сравнивайте результаты одного теста при одинаковых условиях — отсутствие оценки не значит, что модель хуже.

В подключённых источниках результатов для этой модели пока нет.

Что умеет модель

Описание составлено по карточке модели и метаданным Hugging Face

ANIMA (Advanced Nature Inspired Multidisciplinary Assistant) — дообученная Severian языковая модель на архитектуре Mistral с 7,1 млрд параметров. Она предназначена для диалога и генерации текста по научным и философским темам, особенно для решения задач с помощью биомимикрии и этапов биомиметического проектирования.

Модель обучалась на примерах по биомимикрии, биологии, экологии и STEM, а также на научных и философских текстах. README указывает базовую модель ehartford/dolphin-2.0-mistral-7b и контекст до 32 768 токенов. Модель может помогать находить природные аналоги инженерных задач и объяснять научные понятия. Она генерирует ответы, поэтому факты и рекомендации в областях науки и проектирования следует проверять. Лицензия репозитория — Artistic-2.0.

Сценарии применения

  • поиск природных аналогов для инженерной задачи
  • разработка концепций биомиметического дизайна
  • объяснение понятий из биологии и экологии
  • поиск природных подходов к очистке воды
  • ответы на вопросы по научным и философским темам

Ограничения

Ответы модели могут содержать фактические ошибки; научные сведения и проектные рекомендации следует проверять. Языки обучения и заявленная языковая поддержка не подтверждены данными карточки.

Для каких профессий подходит3

Разделы каталога профессий, в задачах которых используется эта модель

Все профессии

Карточка модели

README от автора на Hugging Face, как он его опубликовал

Открыть на Hugging Face

ANIMA-Phi-Neptune-Mistral-7B: Biomimicry Enhanced LLM

Overview

ANIMA (Advanced Nature Inspired Multidisciplinary Assistant) is an expert in various scientific disciplines, including but not limited to biomimicry, biology, and environmental science.

Instagram: @anima_llm


Model Description

ANIMA is fine-tuned on a rich dataset encompassing:

  • 4,000+ Nature-Biomimicry examples
  • 60k Biomimicry Design Process examples
  • 600k STEM facts from Wikipedia
  • Science/Philosophy focused 'All-You-Need-Is-Textbooks' dataset
  • Additional Tree of Knowledge + Biomimicry data combined fine-tuning

The model aims to assist users in solving problems using nature-inspired strategies and concepts.

Special Features

  • Multi-disciplinary Expertise: Knowledge across various scientific and philosophical domains.
  • Biomimicry Design Process: Incorporates a dataset generated by Mistral and Minotaur-15B. The dataset was then intricately processed by a real person to ensure factuality and grounding.

  • Here is a link to The Bloke's GGUF version: ANIMA-Phi-Neptune-Mistral-7B-GGUF

  • ANIMA is also available using Ollama - Download the OLLAMA App (MacOS & Linux) and then run this command in your Terminal 'ollama pull severian/anima' to download the model and then run this command 'ollama run severian/anima' to load the model and start talking.

  • You can also download and use the model with LM Studio (All OS systems). Just download the app and then search for 'ANIMA GGUF' in the search bar and you will have a list of versions to choose from.

  • Want to test ANIMA + Ollama and chat right away? Download the model from Ollama and head here to chat with ANIMA right in your browser! ANIMA - Chat

  • Have a PDF you want to discuss with ANIMA + Ollama? Head here and you can do just that in your browser, 100% locally. ANIMA - Locally Chat over your PDF

  • ANIMA is also being hosted on a Hugging Face Space if you'd like to try it there (It may be slow to generate a respone but it still works just fine) ANIMA - HF Space

  • Contact

If you want to discuss the model/dataset/concept further, have any cool ideas, want to collaborate or if you run into any issues with the model, please don't hesitate to reach out at InterwovenArkitech@Gmail.com

Datasets

  • Severian/Biomimicry (1st Fine-Tune)
  • emrgnt-cmplxty/sciphi-textbooks-are-all-you-need (2nd Fine-Tune)
  • Severian/Bio-Design-Process & fmars/wiki_stem (3rd Fine-Tune)
  • fblgit/tree-of-knowledge & Severian/Biomimicry (4th Fine-Tune)
  • Combined all previous datasets into one giant dataset for a quick fine-tuning for a total of 730k data points (5th Fine-Tune)

Training Stages

  1. Base Model: ehartford/dolphin-2.0-mistral-7b
  2. 1st Fine-Tune: ANIMA-Echo-Mistral-7B-v1 - 1 Epoch x 5hrs on 1 x T4 Small
  3. 2nd Fine-Tune: ANIMA-Phi-Neptune-Mistral-v1-: 1 Epoch x 1hr on 1 x A100
  4. 3rd Fine-Tune: ANIMA-Phi-Neptune-Mistral-7B-v2 - 1 Epoch x 1hr on 1 x A100
  5. 4th Fine-Tune: ANIMA-Phi-Neptune-Mistral-7B-v3 - 1 Epoch x 1hr on 1 x A100
  6. 5th Fine-Tune: ANIMA-Phi-Neptune-Mistral-7B-v4 - 1 Epoch x 15mins on 1 x A100

📝 Usage

ANIMA is designed around the Biomimicry Design Process to guide users in solving problems through biomimicry. For more information and examples, please visit Biomimicry Design Process.

🏁 Benchmarks

  • Average - 62.22
  • ARC - 56.83
  • HellaSwag - 78.82
  • MMLU - 53.84
  • TruthfulQA - 59.40

  • EXAMPLE OUTPUT

    [INST] How can biomimicry help in water purification? [/INST]

Biomimicry plays a significant role in water purification by taking inspiration from natural processes and organisms to develop sustainable and efficient solutions. Here's a comprehensive overview of how biomimicry can help in water purification:

  1. Microbial filtration:

    • Bacteriophages (viruses that infect bacteria) can effectively target contaminants in water systems, such as Escherichia coli and Staphylococcus aureus. By mimicking the natural defense mechanisms of bacteriophages, we can develop efficient filtration systems that remove pollutants from water sources.
    • Some fungi, such as Trametes versicolor, can break down organic pollutants in water using enzymes. This process helps make contaminated water safe for consumption. By studying and replicating these natural processes, we can develop sustainable filtration systems that remove pollutants from water sources effectively.
  2. Biofilm-based filtration:

    • Microorganisms form biofilms on surfaces to protect themselves from environmental threats like predators or contaminants. By mimicking the natural formation of biofilms, we can develop sustainable filtration systems that use bacteria or fungi to remove pollutants from water sources.
    • For example, a study has shown how Pseudomonas putida could be used to biodegrade petroleum hydrocarbons in contaminated water sources using a biofilm-based system. This method offers an eco-friendly alternative to conventional chemical treatments for oil spills.
  3. Wetland-based filtration:

    • Wetlands act as natural filters for water by retaining sediments and organic matter, which helps remove contaminants. By mimicking the natural processes of a wetland, we can design effective water purification systems that use sand filters or engineered wetlands to remove pollutants. For instance, a study has shown how reed beds can be used for water filtration by using them as a natural treatment system for agricultural wastewater.
  4. Sponge filtration:

    • Some marine sponges (e.g., sea sponge) have evolved sophisticated mechanisms to filter out particles and contaminants from the surrounding water. By studying these natural filtration processes, we can develop efficient and sustainable filtration systems that remove pollutants effectively. For example, a study has shown how sea sponges can be used as a model for designing a low-cost, self-sustaining water purification system that uses solar energy to disinfect contaminated water.
  5. Biodegradable materials:

    • Natural organisms have evolved biodegradable materials to protect themselves from predators or environmental threats. For instance, some insects produce natural pesticides (such as alkaloids) to defend themselves against predators. Similarly, we can develop biodegradable materials that release these natural pesticides in contaminated water sources, killing off contaminants and restoring the environment's health.
    • For example, a study has shown how bacterial cells can be used as a natural filtration system to remove contaminants from water sources by incorporating them into membranes that selectively remove organic pollutants.

In conclusion, biomimicry plays a vital role in water purification by using natural processes and organisms as inspiration for developing sustainable and efficient solutions. By studying the natural filtration mechanisms of microorganisms, plants, animals, and other living organisms, we can design eco-friendly and cost-effective water purification systems that help protect our environment from contamination.


Help Support ANIMA


  • KNOWN ISSUES

    The model will sometimes respond to itself and continue the conversation taking both the user and AI roles. This is a known issue in the Mistral model but does not happen very often.

📜 License

This project is licensed under Artistic-2.0

This model is for research purposes only and restricted from any commercial use

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

MetricValue
Avg.55.61
AI2 Reasoning Challenge (25-Shot)55.46
HellaSwag (10-Shot)77.63
MMLU (5-Shot)53.12
TruthfulQA (0-shot)59.01
Winogrande (5-shot)73.48
GSM8k (5-shot)14.94

Как запустить

На своей GPU в ailist. Цена — по текущим предложениям провайдеров

GPU в ailist

Универсальный рантайм

Можно запустить

Подбираем GPU…

Класс архитектуры
MistralForCausalLM
Ревизия весов
54769e3

0 моделей