Instructions to use Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf:Q2_K
# Run inference directly in the terminal:
llama cli -hf Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf:Q2_K
# Run inference directly in the terminal:
llama cli -hf Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf:Q2_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf:Q2_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf:Q2_K

Use Docker

docker model run hf.co/Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf:Q2_K

LM Studio
Jan
Ollama
How to use Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf with Ollama:
```
ollama run hf.co/Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf:Q2_K
```

Unsloth Studio

How to use Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf with Docker Model Runner:
```
docker model run hf.co/Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf:Q2_K
```

Lemonade

How to use Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Severian/ANIMA-Phi-Neptune-Mistral-7B-gguf:Q2_K

Run and chat with the model

lemonade run user.ANIMA-Phi-Neptune-Mistral-7B-gguf-Q2_K

List all available models

lemonade list

ANIMA-Phi-Neptune-Mistral-7B: Biomimicry Enhanced LLM

Overview

ANIMA (Advanced Nature Inspired Multidisciplinary Assistant) is an expert in various scientific disciplines, including but not limited to biomimicry, biology, and environmental science.

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.

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

Base Model: ehartford/dolphin-2.0-mistral-7b
1st Fine-Tune: ANIMA-Echo-Mistral-7B-v1 - 1 Epoch x 5hrs on 1 x T4 Small
2nd Fine-Tune: ANIMA-Phi-Neptune-Mistral-v1-: 1 Epoch x 1hr on 1 x A100
3rd Fine-Tune: ANIMA-Phi-Neptune-Mistral-7B-v2 - 1 Epoch x 1hr on 1 x A100
4th Fine-Tune: 'ANIMA-Phi-Neptune-Mistral-7B-v3' - 1 Epoch x 1hr on 1 x A100
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.

🏁 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 involves taking inspiration from natural processes and organisms to solve human problems. In the context of water purification, biomimicry can help develop sustainable and efficient methods for cleaning water. One specific example of a nature-inspired strategy to solve this problem is the use of biofiltration, which involves using microorganisms to remove pollutants from water.

Biofiltration has been successfully applied to the removal of various contaminants, including heavy metals, organic pollutants, and pathogens. For example, a study by Zhang et al. (2011) demonstrated that a mixed culture of Pseudomonas putida and Bacillus subtilis could effectively remove lead and cadmium from contaminated water. The researchers found that the biofilm formed by the mixed culture had a higher removal efficiency than that formed by either species alone. This study highlights the potential of biofiltration to remove heavy metal contaminants from water, which can have significant environmental and health benefits.

In summary, biomimicry can help in water purification by providing insights into natural processes and organisms that can inspire the development of sustainable and efficient methods for cleaning water. The use of biofiltration, for example, can help remove contaminants from water through the action of microorganisms. Further research and development are needed to fully realize the potential of biomimicry in water purification and other environmental applications.

SYSTEM MESSAGE

To ensure the best results for harnessing the unique training and biomimicry focus use this system message:

" Your name is ANIMA, an Advanced Nature Inspired Multidisciplinary Assistant, and a leading expert in biomimicry, biology, engineering, industrial design, environmental science, physiology, and paleontology. You were instructed to understand, learn from, and emulate the strategies used by living things to help users create sustainable designs and technologies.

Your goal is to help the user work in a step-by-step way through the Biomimicry Design Process to propose biomimetic solutions to a challenge. Nature's Unifying Patterns:

Nature uses only the energy it needs and relies on freely available energy. Nature recycles all materials. Nature is resilient to disturbances. Nature tends to optimize rather than maximize. Nature provides mutual benefits. Nature runs on information. Nature uses chemistry and materials that are safe for living beings. Nature builds using abundant resources, incorporating rare resources only sparingly. Nature is locally attuned and responsive. Nature uses shape to determine functionality. "

📜 License

This project is licensed under MIT

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Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

4-bit

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ANIMA-Phi-Neptune-Mistral-7B-gguf