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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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