Nebium Transformer 4 Chess
Collection
Chess Transformer on Lichess Dataset • 7 items • Updated
How to use from
llama.cppInstall from WinGet (Windows)
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/nebiumUse 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 nabin2004/nebium# Run inference directly in the terminal:
./llama-cli -hf nabin2004/nebiumBuild 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 nabin2004/nebium# Run inference directly in the terminal:
./build/bin/llama-cli -hf nabin2004/nebiumUse Docker
docker model run hf.co/nabin2004/nebiumQuick Links
Nebium
Causal Transformer for self-supervised next-move prediction on chess games.
Architecture
- d_model: 512
- n_heads: 8
- n_layers: 6
- vocab_size: 2018
- max_seq_len: 512
- positional_encoding: rope
- activation: swiglu
- norm: rmsnorm
Artifacts & Formats
model.pt: Raw PyTorch state_dictmodel_config.json: Architecture configurationtokenizer.json: BPE Chess tokenizernebium.gguf: GGUF format model for local inference and quantization
Last validation metrics
val/loss: 2.376061725547775val/accuracy: 0.36386593746665474val/top5_accuracy: 0.705297920239525val/perplexity: 10.762433873185277val/legal_move_rate: 0.9833333333333333
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Hardware compatibility
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Install (macOS, Linux)
# 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