Instructions to use teknium/Hermes-Trismegistus-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use teknium/Hermes-Trismegistus-Mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="teknium/Hermes-Trismegistus-Mistral-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("teknium/Hermes-Trismegistus-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("teknium/Hermes-Trismegistus-Mistral-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use teknium/Hermes-Trismegistus-Mistral-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "teknium/Hermes-Trismegistus-Mistral-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "teknium/Hermes-Trismegistus-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/teknium/Hermes-Trismegistus-Mistral-7B

SGLang

How to use teknium/Hermes-Trismegistus-Mistral-7B 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 "teknium/Hermes-Trismegistus-Mistral-7B" \
    --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": "teknium/Hermes-Trismegistus-Mistral-7B",
		"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 "teknium/Hermes-Trismegistus-Mistral-7B" \
        --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": "teknium/Hermes-Trismegistus-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use teknium/Hermes-Trismegistus-Mistral-7B with Docker Model Runner:
```
docker model run hf.co/teknium/Hermes-Trismegistus-Mistral-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Description:

Transcendence is All You Need! Mistral Trismegistus is a model made for people interested in the esoteric, occult, and spiritual.

Trismegistus evolved, trained over Hermes 2.5, the model performs far better in all tasks, including esoteric tasks!

The change between Mistral-Trismegistus and Hermes-Trismegistus is that this version trained over hermes 2.5 instead of the base mistral model, this means it is full of task capabilities that it Trismegistus can utilize for all esoteric and occult tasks, and performs them far better than ever before.

Here are some outputs:

Acknowledgements:

Special thanks to @a16z.

Dataset:

This model was trained on a 100% synthetic, gpt-4 generated dataset, about ~10,000 examples, on a wide and diverse set of both tasks and knowledge about the esoteric, occult, and spiritual.

The dataset will be released soon!

Usage:

Prompt Format:

USER: <prompt>
ASSISTANT:

<system message>
USER: <prompt>
ASSISTANT:

Benchmarks:

No benchmark can capture the nature and essense of the quality of spirituality and esoteric knowledge and tasks. You will have to try testing it yourself!

Training run on wandb here: https://wandb.ai/teknium1/occult-expert-mistral-7b/runs/coccult-expert-mistral-6/overview