Instructions to use tsunemoto/cosmo-1b-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 tsunemoto/cosmo-1b-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 tsunemoto/cosmo-1b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf tsunemoto/cosmo-1b-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 tsunemoto/cosmo-1b-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf tsunemoto/cosmo-1b-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 tsunemoto/cosmo-1b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf tsunemoto/cosmo-1b-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 tsunemoto/cosmo-1b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf tsunemoto/cosmo-1b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/tsunemoto/cosmo-1b-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use tsunemoto/cosmo-1b-GGUF with Ollama:
ollama run hf.co/tsunemoto/cosmo-1b-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use tsunemoto/cosmo-1b-GGUF with Docker Model Runner:
docker model run hf.co/tsunemoto/cosmo-1b-GGUF:Q4_K_M
- Lemonade
How to use tsunemoto/cosmo-1b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tsunemoto/cosmo-1b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.cosmo-1b-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
- Xet hash:
- a3664c3ce58cfe805b842f6529c83f1a6e8eba403aa77c63e9f311d78fb80000
- Size of remote file:
- 1.31 GB
- SHA256:
- 28eefab2de7053b6bf165e62cd4a33cfca987b293bcd079cfb5309ab639449d7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.