Instructions to use Severian/ANIMA-Cognitive-Mistral-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Severian/ANIMA-Cognitive-Mistral-v1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Severian/ANIMA-Cognitive-Mistral-v1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Severian/ANIMA-Cognitive-Mistral-v1")
model = AutoModelForCausalLM.from_pretrained("Severian/ANIMA-Cognitive-Mistral-v1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Severian/ANIMA-Cognitive-Mistral-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Severian/ANIMA-Cognitive-Mistral-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Severian/ANIMA-Cognitive-Mistral-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Severian/ANIMA-Cognitive-Mistral-v1

SGLang

How to use Severian/ANIMA-Cognitive-Mistral-v1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Severian/ANIMA-Cognitive-Mistral-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Severian/ANIMA-Cognitive-Mistral-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Severian/ANIMA-Cognitive-Mistral-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Severian/ANIMA-Cognitive-Mistral-v1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Severian/ANIMA-Cognitive-Mistral-v1 with Docker Model Runner:
```
docker model run hf.co/Severian/ANIMA-Cognitive-Mistral-v1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Base Model: `CollectiveCognition-v1-Mistral-7B`

Tags:

ANIMA (Advanced Nature Inspired Multidisciplinary Assistant)
Biomimicry
Fine-tuned
Scientific prompts

Datasets:

Severian/Biomimicry

Model Index:

Name: ANIMA-Cognitive-Mistral-v1

License: Apache 2.0

Language: English

ANIMA - Advanced Nature Inspired Multidisciplinary Assistant

Model Description:

ANIMA is designed as a leading expert in various scientific disciplines including biomimicry, biology, and environmental science. It is fine-tuned on a dataset of over 4,000 high-quality scientific and accurate prompts to help users through the Biomimicry Design Process. The model is intended to propose biomimetic solutions to challenges while frequently asking for user feedback or clarification.

Special Features:

High-Quality Dataset: Trained on more than 4,000 scientific and accurate prompts related to biomimicry.
Multi-disciplinary Expertise: Covers biomimicry, biology, engineering, industrial design, and more.
User-Centric Design: Emphasizes frequent user feedback and clarification.

Usage:

ANIMA follows a structured Biomimicry Design Process, guiding users through steps such as Define, Biologize, Discover, Abstract, and Emulate. For usage instructions and examples, please visit Biomimicry Design Process.

Performance Metrics:

To be updated

Dataset:

The model is trained on a high-quality dataset from various scientific disciplines. For more details and to contribute, visit Severian/Biomimicry.

Benchmarks:

To be updated

Training:

To be updated

Licensing:

MIT

Downloads last month: 979

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Severian/ANIMA-Cognitive-Mistral-v1

Quantizations

3 models

Severian
/

ANIMA-Cognitive-Mistral-v1

Base Model: `CollectiveCognition-v1-Mistral-7B`

Datasets:

Model Index:

License: Apache 2.0

Language: English

ANIMA - Advanced Nature Inspired Multidisciplinary Assistant

Model Description:

Special Features:

Usage:

Performance Metrics:

Dataset:

Benchmarks:

Training:

Licensing:

Model tree for Severian/ANIMA-Cognitive-Mistral-v1

Dataset used to train Severian/ANIMA-Cognitive-Mistral-v1

Spaces using Severian/ANIMA-Cognitive-Mistral-v1 10

Base Model: CollectiveCognition-v1-Mistral-7B

Datasets:

Model Index:

License: Apache 2.0

Language: English

ANIMA - Advanced Nature Inspired Multidisciplinary Assistant

Model Description:

Special Features:

Usage:

Performance Metrics:

Dataset:

Benchmarks:

Training:

Licensing:

Model tree for Severian/ANIMA-Cognitive-Mistral-v1

Dataset used to train Severian/ANIMA-Cognitive-Mistral-v1

Spaces using Severian/ANIMA-Cognitive-Mistral-v1 10

Base Model: `CollectiveCognition-v1-Mistral-7B`