Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+anima-phi-neptune-mistral-7b.Q4_0.gguf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,305 @@
+---
+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|
+

anima-phi-neptune-mistral-7b.Q4_0.gguf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:de5037215e021cb2d44564cd91b602607b8f8d58448f337a920122c7c4cc1cef
+size 4108917152