The Gamunu Project
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3 items • Updated
How to use manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF:BF16
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF:BF16 # Run inference directly in the terminal: llama cli -hf manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF:BF16
# 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 manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF:BF16 # Run inference directly in the terminal: ./llama-cli -hf manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF:BF16
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 manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF:BF16
docker model run hf.co/manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF:BF16
How to use manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF with vLLM:
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}'docker model run hf.co/manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF:BF16
How to use manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF with Ollama:
ollama run hf.co/manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF:BF16
How to use manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF with Unsloth Studio:
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 manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF to start chatting
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 manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF to start chatting
How to use manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF with Docker Model Runner:
docker model run hf.co/manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF:BF16
How to use manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF:BF16
lemonade run user.Gamunu-4B-Instruct-Alpha-GGUF-BF16
lemonade list
These models was converted to GGUF format from manthilaffs/Gamunu-4B-Instruct-Alpha using llama.cpp.
Refer to the original model card for more details on the model.
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
llama-cli --hf-repo manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF --hf-file gamunu-4b-instruct-alpha-BF16.gguf -p "Hello! how are you?"
llama-cli --hf-repo manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF --hf-file gamunu-4b-instruct-alpha-q8_0.gguf -p "Hello! how are you?"
llama-server --hf-repo manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF --hf-file gamunu-4b-instruct-alpha-BF16.gguf -c 2048
llama-server --hf-repo manthilaffs/Gamunu-4B-Instruct-Alpha-GGUF --hf-file gamunu-4b-instruct-alpha-q8_0.gguf -c 2048
8-bit
16-bit
Base model
google/gemma-3-4b-pt