How to use AKASH2393/ty-emotune with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf AKASH2393/ty-emotune # Run inference directly in the terminal: llama cli -hf AKASH2393/ty-emotune
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AKASH2393/ty-emotune # Run inference directly in the terminal: llama cli -hf AKASH2393/ty-emotune
# 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 AKASH2393/ty-emotune # Run inference directly in the terminal: ./llama-cli -hf AKASH2393/ty-emotune
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 AKASH2393/ty-emotune # Run inference directly in the terminal: ./build/bin/llama-cli -hf AKASH2393/ty-emotune
docker model run hf.co/AKASH2393/ty-emotune
How to use AKASH2393/ty-emotune with Ollama:
ollama run hf.co/AKASH2393/ty-emotune
How to use AKASH2393/ty-emotune with Docker Model Runner:
How to use AKASH2393/ty-emotune with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AKASH2393/ty-emotune
lemonade run user.ty-emotune-{{QUANT_TAG}}
lemonade list
https://huggingface.co/AKASH2393/ty-emotune/resolve/main/tokenizer.json
hf download hf://AKASH2393/ty-emotune/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/AKASH2393/ty-emotune/resolve/main/tokenizer.json
