| --- |
| 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 |
|
|
| <img src="https://cdn-uploads.huggingface.co/production/uploads/64740cf7485a7c8e1bd51ac9/JZH6p50t_j3-OUph4Wq6y.png" width="500"> |
|
|
| ## 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](https://www.instagram.com/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](https://huggingface.co/TheBloke/ANIMA-Phi-Neptune-Mistral-7B-GGUF) |
|
|
|
|
| - 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. |
|
|
|
|
| - 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. |
|
|
|
|
| - 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/) |
|
|
| - 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/) |
|
|
| - 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) |
|
|
| - **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](https://toolbox.biomimicry.org/methods/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](https://www.buymeacoffee.com/anima7b)*** |
|
|
| --- |
|
|
| - **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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
| Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Severian__ANIMA-Phi-Neptune-Mistral-7B-v4) |
|
|
| | 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| |
|
|
|
|
