Instructions to use mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.3-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/Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.3-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/Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.3-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/Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.3-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/Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF with Ollama:
ollama run hf.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Llama-3.1-Swallow-8B-Instruct-v0.3-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
- Xet hash:
- e23ee830980198d8cda508f7ce681a8a8d3f11f8fcafaf6900d3da335a228776
- Size of remote file:
- 5.6 GB
- SHA256:
- 492b19d58d6742a073866a85e76c918a4b7b1d09290ae8afb4a48b9a2443bc8c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.