metadata

license: artistic-2.0
tags:
  - chemistry
  - biology
  - climate
  - science
  - philosophy
  - nature
  - ecology
  - biomimicry
  - fauna
  - flora
datasets:
  - Severian/Biomimicry
  - emrgnt-cmplxty/sciphi-textbooks-are-all-you-need
  - fmars/wiki_stem
  - fblgit/tree-of-knowledge
  - Severian/Bio-Design-Process
metrics:
  - accuracy
pipeline_tag: text-generation
model-index:
  - name: ANIMA-Phi-Neptune-Mistral-7B-v4
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 55.46
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Severian/ANIMA-Phi-Neptune-Mistral-7B-v4
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 77.63
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Severian/ANIMA-Phi-Neptune-Mistral-7B-v4
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 53.12
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Severian/ANIMA-Phi-Neptune-Mistral-7B-v4
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 59.01
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Severian/ANIMA-Phi-Neptune-Mistral-7B-v4
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 73.48
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Severian/ANIMA-Phi-Neptune-Mistral-7B-v4
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 14.94
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Severian/ANIMA-Phi-Neptune-Mistral-7B-v4
          name: Open LLM Leaderboard

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

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

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.
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.
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.
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.
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

Metric	Value
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