Instructions to use tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use tensorblock/Llama-3-SauerkrautLM-8b-Instruct-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 tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q2_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q2_K # Run inference directly in the terminal: llama cli -hf tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q2_K
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 tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q2_K # Run inference directly in the terminal: ./llama-cli -hf tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q2_K
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 tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q2_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q2_K
Use Docker
docker model run hf.co/tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q2_K
- LM Studio
- Jan
- Ollama
How to use tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF with Ollama:
ollama run hf.co/tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q2_K
- Unsloth Desktop
- Docker Model Runner
How to use tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q2_K
- Lemonade
How to use tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF:Q2_K
Run and chat with the model
lemonade run user.Llama-3-SauerkrautLM-8b-Instruct-GGUF-Q2_K
List all available models
lemonade list
- Atomic Chat
Download Llama-3-SauerkrautLM-8b-Instruct-Q4_K_S.gguf from tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 4.69 GB
-
https://huggingface.co/tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF/resolve/fcdfdcdbbd50c606ade088bd249fae8132dce78a/Llama-3-SauerkrautLM-8b-Instruct-Q4_K_S.gguf
- Command line
-
hf download hf://tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF@fcdfdcdbbd50c606ade088bd249fae8132dce78a/Llama-3-SauerkrautLM-8b-Instruct-Q4_K_S.gguf
-
curl -L -o Llama-3-SauerkrautLM-8b-Instruct-Q4_K_S.gguf https://huggingface.co/tensorblock/Llama-3-SauerkrautLM-8b-Instruct-GGUF/resolve/fcdfdcdbbd50c606ade088bd249fae8132dce78a/Llama-3-SauerkrautLM-8b-Instruct-Q4_K_S.gguf
4.69 GB
- Xet hash:
- ac2322126b7d8100777ddb6b04f3fc4a2d0b2afb7df99d05af78f45cbb0cc282
- Size of remote file:
- 4.69 GB
- SHA256:
- b8bb7905385cd7842f29415a12494f5caed2e146a79e1650091a71cdd280acc1
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