How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bklassen3/softbot:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bklassen3/softbot:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf bklassen3/softbot:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf bklassen3/softbot:Q4_K_M
Use 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 bklassen3/softbot:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bklassen3/softbot:Q4_K_M
Build 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 bklassen3/softbot:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bklassen3/softbot:Q4_K_M
Use Docker
docker model run hf.co/bklassen3/softbot:Q4_K_M
Quick Links

Softball-Q4 (GGUF)

Format: GGUF (compatible with llama.cpp, llamafile, many runtimes)
Size: ~4.9 GB (q4_K_M quant)
Base model: Meta-Llama-3-8B-Instruct Finetuning: LoRA merged on 09-12-2025, domain: softball analytics Q&A & text-to-SQL.

Intended Use

  • Natural language Q&A and SQL assistance for softball data.
  • Educational and exploratory analysis assistance.

Not intended for: safety-critical decisions or authoritative rule enforcement.

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GGUF
Model size
8B params
Architecture
llama
Hardware compatibility
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4-bit

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