Nebium Transformer 4 Chess
Collection
Chess Transformer on Lichess Dataset โข 7 items โข Updated
How to use nabin2004/nebium with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf nabin2004/nebium # Run inference directly in the terminal: llama cli -hf nabin2004/nebium
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf nabin2004/nebium # Run inference directly in the terminal: llama cli -hf nabin2004/nebium
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf nabin2004/nebium # Run inference directly in the terminal: ./build/bin/llama cli -hf nabin2004/nebium
docker model run hf.co/nabin2004/nebium
How to use nabin2004/nebium with Ollama:
ollama run hf.co/nabin2004/nebium
How to use nabin2004/nebium with Docker Model Runner:
docker model run hf.co/nabin2004/nebium
How to use nabin2004/nebium with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull nabin2004/nebium
lemonade run user.nebium-{{QUANT_TAG}}lemonade list
Causal Transformer for self-supervised next-move prediction on chess games.
model.pt: Raw PyTorch state_dictmodel_config.json: Architecture configurationtokenizer.json: BPE Chess tokenizernebium.gguf: GGUF format model for local inference and quantizationval/loss: 2.376061725547775val/accuracy: 0.36386593746665474val/top5_accuracy: 0.705297920239525val/perplexity: 10.762433873185277val/legal_move_rate: 0.9833333333333333We're not able to determine the quantization variants.