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Model: Severian/ANIMA-Phi-Neptune-Mistral-7B Source: Original Platform
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README.md
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---
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license: artistic-2.0
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tags:
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- chemistry
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- biology
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- climate
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- science
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- philosophy
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- nature
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- ecology
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- biomimicry
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- fauna
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- flora
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datasets:
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- Severian/Biomimicry
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- emrgnt-cmplxty/sciphi-textbooks-are-all-you-need
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- fmars/wiki_stem
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- fblgit/tree-of-knowledge
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- Severian/Bio-Design-Process
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metrics:
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- accuracy
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pipeline_tag: text-generation
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model-index:
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- name: ANIMA-Phi-Neptune-Mistral-7B-v4
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results:
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: acc_norm
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value: 55.46
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Severian/ANIMA-Phi-Neptune-Mistral-7B-v4
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: acc_norm
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value: 77.63
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name: normalized accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Severian/ANIMA-Phi-Neptune-Mistral-7B-v4
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 53.12
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Severian/ANIMA-Phi-Neptune-Mistral-7B-v4
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: mc2
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value: 59.01
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Severian/ANIMA-Phi-Neptune-Mistral-7B-v4
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 73.48
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Severian/ANIMA-Phi-Neptune-Mistral-7B-v4
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name: Open LLM Leaderboard
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- task:
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type: text-generation
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name: Text Generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: acc
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value: 14.94
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name: accuracy
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source:
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url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Severian/ANIMA-Phi-Neptune-Mistral-7B-v4
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name: Open LLM Leaderboard
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---
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# ANIMA-Phi-Neptune-Mistral-7B: Biomimicry Enhanced LLM
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<img src="https://cdn-uploads.huggingface.co/production/uploads/64740cf7485a7c8e1bd51ac9/JZH6p50t_j3-OUph4Wq6y.png" width="500">
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## Overview
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**ANIMA** (Advanced Nature Inspired Multidisciplinary Assistant) is an expert in various scientific disciplines, including but not limited to biomimicry, biology, and environmental science.
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**Instagram: [@anima_llm](https://www.instagram.com/anima_llm)**
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---
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## Model Description
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ANIMA is fine-tuned on a rich dataset encompassing:
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- 4,000+ Nature-Biomimicry examples
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- 60k Biomimicry Design Process examples
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- 600k STEM facts from Wikipedia
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- Science/Philosophy focused 'All-You-Need-Is-Textbooks' dataset
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- Additional Tree of Knowledge + Biomimicry data combined fine-tuning
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The model aims to assist users in solving problems using nature-inspired strategies and concepts.
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### Special Features
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- **Multi-disciplinary Expertise**: Knowledge across various scientific and philosophical domains.
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- **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.
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---
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- Here is a link to The Bloke's GGUF version: [ANIMA-Phi-Neptune-Mistral-7B-GGUF](https://huggingface.co/TheBloke/ANIMA-Phi-Neptune-Mistral-7B-GGUF)
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- ANIMA is also available using Ollama - Download the [OLLAMA](https://ollama.ai/) 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.
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- You can also download and use the model with [LM Studio](https://lmstudio.ai/) (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.
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- 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](https://severian42.github.io/ANIMA-Chat/)
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- 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](https://ANIMA-pdf-chat.vercel.app/)
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- 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](https://huggingface.co/spaces/Severian/ANIMA-7B-Biomimicry-LLM)
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- **Contact**
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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
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## Datasets
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- `Severian/Biomimicry` (1st Fine-Tune)
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- `emrgnt-cmplxty/sciphi-textbooks-are-all-you-need` (2nd Fine-Tune)
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- `Severian/Bio-Design-Process` & `fmars/wiki_stem` (3rd Fine-Tune)
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- `fblgit/tree-of-knowledge` & `Severian/Biomimicry` (4th Fine-Tune)
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- `Combined all previous datasets` into one giant dataset for a quick fine-tuning for a total of 730k data points (5th Fine-Tune)
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## Training Stages
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1. **Base Model**: `ehartford/dolphin-2.0-mistral-7b`
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2. **1st Fine-Tune**: `ANIMA-Echo-Mistral-7B-v1` - 1 Epoch x 5hrs on 1 x T4 Small
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3. **2nd Fine-Tune**: `ANIMA-Phi-Neptune-Mistral-v1`-: 1 Epoch x 1hr on 1 x A100
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4. **3rd Fine-Tune**: `ANIMA-Phi-Neptune-Mistral-7B-v2` - 1 Epoch x 1hr on 1 x A100
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5. **4th Fine-Tune**: `ANIMA-Phi-Neptune-Mistral-7B-v3` - 1 Epoch x 1hr on 1 x A100
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6. **5th Fine-Tune**: `ANIMA-Phi-Neptune-Mistral-7B-v4` - 1 Epoch x 15mins on 1 x A100
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---
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## 📝 Usage
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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](https://toolbox.biomimicry.org/methods/process/).
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## 🏁 Benchmarks
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- Average - 62.22
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- ARC - 56.83
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- HellaSwag - 78.82
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- MMLU - 53.84
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- TruthfulQA - 59.40
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---
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- **EXAMPLE OUTPUT**
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[INST] How can biomimicry help in water purification? [/INST]
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Biomimicry plays a significant role in water purification by taking
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inspiration from natural processes and organisms to develop sustainable
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and efficient solutions. Here's a comprehensive overview of how biomimicry
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can help in water purification:
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1. Microbial filtration:
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- Bacteriophages (viruses that infect bacteria) can effectively target
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contaminants in water systems, such as Escherichia coli and Staphylococcus
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aureus. By mimicking the natural defense mechanisms of bacteriophages, we
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can develop efficient filtration systems that remove pollutants from water
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sources.
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- Some fungi, such as Trametes versicolor, can break down organic
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pollutants in water using enzymes. This process helps make contaminated
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water safe for consumption. By studying and replicating these natural
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processes, we can develop sustainable filtration systems that remove
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pollutants from water sources effectively.
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2. Biofilm-based filtration:
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- Microorganisms form biofilms on surfaces to protect themselves from
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environmental threats like predators or contaminants. By mimicking the
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natural formation of biofilms, we can develop sustainable filtration
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systems that use bacteria or fungi to remove pollutants from water
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sources.
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- For example, a study has shown how Pseudomonas putida could
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be used to biodegrade petroleum hydrocarbons in contaminated water sources
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using a biofilm-based system. This method offers an eco-friendly
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alternative to conventional chemical treatments for oil spills.
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3. Wetland-based filtration:
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- Wetlands act as natural filters for water by retaining sediments and
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organic matter, which helps remove contaminants. By mimicking the natural
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processes of a wetland, we can design effective water purification systems
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that use sand filters or engineered wetlands to remove pollutants. For
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instance, a study has shown how reed beds can be used for water filtration
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by using them as a natural treatment system for agricultural wastewater.
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4. Sponge filtration:
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- Some marine sponges (e.g., sea sponge) have evolved sophisticated
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mechanisms to filter out particles and contaminants from the surrounding
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water. By studying these natural filtration processes, we can develop
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efficient and sustainable filtration systems that remove pollutants
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effectively. For example, a study has shown how sea sponges can be used as
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a model for designing a low-cost, self-sustaining water purification
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system that uses solar energy to disinfect contaminated water.
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5. Biodegradable materials:
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- Natural organisms have evolved biodegradable materials to protect
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themselves from predators or environmental threats. For instance, some
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insects produce natural pesticides (such as alkaloids) to defend
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themselves against predators. Similarly, we can develop biodegradable
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materials that release these natural pesticides in contaminated water
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sources, killing off contaminants and restoring the environment's health.
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- For example, a study has shown how bacterial cells can be used as a
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natural filtration system to remove contaminants from water sources by
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incorporating them into membranes that selectively remove organic
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pollutants.
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In conclusion, biomimicry plays a vital role in water purification by
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using natural processes and organisms as inspiration for developing
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sustainable and efficient solutions. By studying the natural filtration
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mechanisms of microorganisms, plants, animals, and other living organisms,
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we can design eco-friendly and cost-effective water purification systems
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that help protect our environment from contamination.
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---
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### ***[Help Support ANIMA](https://www.buymeacoffee.com/anima7b)***
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---
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- **KNOWN ISSUES**
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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.
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## 📜 License
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This project is licensed under Artistic-2.0
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**This model is for research purposes only and restricted from any commercial use**
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Severian__ANIMA-Phi-Neptune-Mistral-7B-v4)
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| Metric |Value|
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|---------------------------------|----:|
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|Avg. |55.61|
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|AI2 Reasoning Challenge (25-Shot)|55.46|
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|HellaSwag (10-Shot) |77.63|
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|MMLU (5-Shot) |53.12|
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|TruthfulQA (0-shot) |59.01|
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|Winogrande (5-shot) |73.48|
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|GSM8k (5-shot) |14.94|
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