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Severian/ANIMA-Phi-Neptune-Mistral-7B
ANIMA-Phi-Neptune-Mistral-7B is a text generation model from Severian. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as artistic-2.0.
<img src="https://cdn-uploads.huggingface.co/production/uploads/64740cf7485a7c8e1bd51ac9/JZH6p50tj3-OUph4Wq6y.png" width="500"
Downloads · 30 days
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From the Hugging Face model README
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
ANIMA is fine-tuned on a rich dataset encompassing:
The model aims to assist users in solving problems using nature-inspired strategies and concepts.
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 [email protected]
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)ehartford/dolphin-2.0-mistral-7bANIMA-Echo-Mistral-7B-v1 - 1 Epoch x 5hrs on 1 x T4 SmallANIMA-Phi-Neptune-Mistral-v1-: 1 Epoch x 1hr on 1 x A100ANIMA-Phi-Neptune-Mistral-7B-v2 - 1 Epoch x 1hr on 1 x A100ANIMA-Phi-Neptune-Mistral-7B-v3 - 1 Epoch x 1hr on 1 x A100ANIMA-Phi-Neptune-Mistral-7B-v4 - 1 Epoch x 15mins on 1 x A100ANIMA 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.
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:
Biofilm-based filtration:
Wetland-based filtration:
Sponge filtration:
Biodegradable materials:
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.
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.
This project is licensed under Artistic-2.0
This model is for research purposes only and restricted from any commercial use
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 |