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

Libraries

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

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

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

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("afrideva/Hermes-Trismegistus-Mistral-7B-GGUF", dtype="auto")

llama-cpp-python

How to use afrideva/Hermes-Trismegistus-Mistral-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="afrideva/Hermes-Trismegistus-Mistral-7B-GGUF",
	filename="hermes-trismegistus-mistral-7b.q2_k.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use afrideva/Hermes-Trismegistus-Mistral-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf afrideva/Hermes-Trismegistus-Mistral-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/Hermes-Trismegistus-Mistral-7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf afrideva/Hermes-Trismegistus-Mistral-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf afrideva/Hermes-Trismegistus-Mistral-7B-GGUF:Q4_K_M

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 afrideva/Hermes-Trismegistus-Mistral-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf afrideva/Hermes-Trismegistus-Mistral-7B-GGUF:Q4_K_M

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 afrideva/Hermes-Trismegistus-Mistral-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf afrideva/Hermes-Trismegistus-Mistral-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/afrideva/Hermes-Trismegistus-Mistral-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

docker model run hf.co/afrideva/Hermes-Trismegistus-Mistral-7B-GGUF:Q4_K_M

SGLang

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

Ollama
How to use afrideva/Hermes-Trismegistus-Mistral-7B-GGUF with Ollama:
```
ollama run hf.co/afrideva/Hermes-Trismegistus-Mistral-7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use afrideva/Hermes-Trismegistus-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 afrideva/Hermes-Trismegistus-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 afrideva/Hermes-Trismegistus-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 afrideva/Hermes-Trismegistus-Mistral-7B-GGUF to start chatting

Docker Model Runner
How to use afrideva/Hermes-Trismegistus-Mistral-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/afrideva/Hermes-Trismegistus-Mistral-7B-GGUF:Q4_K_M
```

Lemonade

How to use afrideva/Hermes-Trismegistus-Mistral-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull afrideva/Hermes-Trismegistus-Mistral-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Hermes-Trismegistus-Mistral-7B-GGUF-Q4_K_M

List all available models

lemonade list

teknium/Hermes-Trismegistus-Mistral-7B-GGUF

Quantized GGUF model files for Hermes-Trismegistus-Mistral-7B from teknium

Name	Quant method	Size
hermes-trismegistus-mistral-7b.q2_k.gguf	q2_k	3.08 GB
hermes-trismegistus-mistral-7b.q3_k_m.gguf	q3_k_m	3.52 GB
hermes-trismegistus-mistral-7b.q4_k_m.gguf	q4_k_m	4.37 GB
hermes-trismegistus-mistral-7b.q5_k_m.gguf	q5_k_m	5.13 GB
hermes-trismegistus-mistral-7b.q6_k.gguf	q6_k	5.94 GB
hermes-trismegistus-mistral-7b.q8_0.gguf	q8_0	7.70 GB

Original Model Card:

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

Licensing:

Apache 2.0

Downloads last month: 158

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for afrideva/Hermes-Trismegistus-Mistral-7B-GGUF

Base model

teknium/Hermes-Trismegistus-Mistral-7B

Quantized

(5)

this model

afrideva
/

Hermes-Trismegistus-Mistral-7B-GGUF