How to use RaspizdAI/pizdecM2 with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf RaspizdAI/pizdecM2 # Run inference directly in the terminal: llama cli -hf RaspizdAI/pizdecM2
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf RaspizdAI/pizdecM2 # Run inference directly in the terminal: llama cli -hf RaspizdAI/pizdecM2
# 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 RaspizdAI/pizdecM2 # Run inference directly in the terminal: ./llama-cli -hf RaspizdAI/pizdecM2
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 RaspizdAI/pizdecM2 # Run inference directly in the terminal: ./build/bin/llama-cli -hf RaspizdAI/pizdecM2
docker model run hf.co/RaspizdAI/pizdecM2
How to use RaspizdAI/pizdecM2 with vLLM:
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RaspizdAI/pizdecM2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RaspizdAI/pizdecM2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'
How to use RaspizdAI/pizdecM2 with Ollama:
ollama run hf.co/RaspizdAI/pizdecM2
How to use RaspizdAI/pizdecM2 with Docker Model Runner:
How to use RaspizdAI/pizdecM2 with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RaspizdAI/pizdecM2
lemonade run user.pizdecM2-{{QUANT_TAG}}
lemonade list
https://huggingface.co/RaspizdAI/pizdecM2/resolve/main/tokenizer.json
hf download hf://RaspizdAI/pizdecM2/tokenizer.json
curl -L -o tokenizer.json https://huggingface.co/RaspizdAI/pizdecM2/resolve/main/tokenizer.json
