Instructions to use mradermacher/Saul-7B-Instruct-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/Saul-7B-Instruct-v1-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/Saul-7B-Instruct-v1-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/Saul-7B-Instruct-v1-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 mradermacher/Saul-7B-Instruct-v1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/Saul-7B-Instruct-v1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/Saul-7B-Instruct-v1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/Saul-7B-Instruct-v1-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 mradermacher/Saul-7B-Instruct-v1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/Saul-7B-Instruct-v1-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 mradermacher/Saul-7B-Instruct-v1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/Saul-7B-Instruct-v1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/Saul-7B-Instruct-v1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/Saul-7B-Instruct-v1-GGUF with Ollama:
ollama run hf.co/mradermacher/Saul-7B-Instruct-v1-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mradermacher/Saul-7B-Instruct-v1-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/Saul-7B-Instruct-v1-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/Saul-7B-Instruct-v1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/Saul-7B-Instruct-v1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Saul-7B-Instruct-v1-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download Saul-7B-Instruct-v1.Q2_K.gguf from mradermacher/Saul-7B-Instruct-v1-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 2.72 GB
-
https://huggingface.co/mradermacher/Saul-7B-Instruct-v1-GGUF/resolve/22352cce085b5006ec273fbe2ee08cc39743052a/Saul-7B-Instruct-v1.Q2_K.gguf
- Command line
-
hf download hf://mradermacher/Saul-7B-Instruct-v1-GGUF@22352cce085b5006ec273fbe2ee08cc39743052a/Saul-7B-Instruct-v1.Q2_K.gguf
-
curl -L -o Saul-7B-Instruct-v1.Q2_K.gguf https://huggingface.co/mradermacher/Saul-7B-Instruct-v1-GGUF/resolve/22352cce085b5006ec273fbe2ee08cc39743052a/Saul-7B-Instruct-v1.Q2_K.gguf
2.72 GB
- Xet hash:
- b8bfb6c6d7998595d8d5d8a6460de8db58e8e2451f5b9d03b7610decf53b3a2c
- Size of remote file:
- 2.72 GB
- SHA256:
- 849e892dddcf1ff6dca86474be6de8b47376e28bd396a7550db6ff9d629fe686
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.