Instructions to use MaziyarPanahi/MistraMystic-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 MaziyarPanahi/MistraMystic-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 MaziyarPanahi/MistraMystic-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf MaziyarPanahi/MistraMystic-GGUF:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MaziyarPanahi/MistraMystic-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf MaziyarPanahi/MistraMystic-GGUF:Q5_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 MaziyarPanahi/MistraMystic-GGUF:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf MaziyarPanahi/MistraMystic-GGUF:Q5_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 MaziyarPanahi/MistraMystic-GGUF:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MaziyarPanahi/MistraMystic-GGUF:Q5_K_M

Use Docker

docker model run hf.co/MaziyarPanahi/MistraMystic-GGUF:Q5_K_M

LM Studio
Jan

vLLM

How to use MaziyarPanahi/MistraMystic-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/MistraMystic-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaziyarPanahi/MistraMystic-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/MistraMystic-GGUF:Q5_K_M

Ollama
How to use MaziyarPanahi/MistraMystic-GGUF with Ollama:
```
ollama run hf.co/MaziyarPanahi/MistraMystic-GGUF:Q5_K_M
```

Unsloth Studio

How to use MaziyarPanahi/MistraMystic-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 MaziyarPanahi/MistraMystic-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 MaziyarPanahi/MistraMystic-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MaziyarPanahi/MistraMystic-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use MaziyarPanahi/MistraMystic-GGUF with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/MistraMystic-GGUF:Q5_K_M
```

Lemonade

How to use MaziyarPanahi/MistraMystic-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MaziyarPanahi/MistraMystic-GGUF:Q5_K_M

Run and chat with the model

lemonade run user.MistraMystic-GGUF-Q5_K_M

List all available models

lemonade list

MistraMystic-GGUF / MistraMystic.Q5_K_M.gguf

MaziyarPanahi

Upload folder using huggingface_hub (#1)

a5e1abc verified almost 2 years ago

Download

History Blame GGUF Editor new Delete

5.14 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 3313e4b59231ee59caf51afbcfe44dc0f0d80ad05700144f3d86c44a1f97466e
Size of remote file:: 5.14 GB
SHA256:: 3b20767b3e73299c980680c312e1f0c420296f621b0c0a8d6818e041671a7544

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.