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 QuantFactory/Captain_BMO-12B-GGUF:
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Captain_BMO-12B-GGUF:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Captain_BMO-12B-GGUF:
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Captain_BMO-12B-GGUF:
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 QuantFactory/Captain_BMO-12B-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Captain_BMO-12B-GGUF:
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 QuantFactory/Captain_BMO-12B-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Captain_BMO-12B-GGUF:
Use Docker
docker model run hf.co/QuantFactory/Captain_BMO-12B-GGUF:
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QuantFactory/Captain_BMO-12B-GGUF

This is quantized version of Nitral-AI/Captain_BMO-12B created using llama.cpp

Original Model Card

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Quant's available from Bartowski <3: GGUF Single Quant from me: 4bpw Exl2

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Uses Mistral Formatting, Text completion preset here

Notes: One off train most likely, this was done purely for internal testing purposes but seemed ok enough to release. I do not plan to offer any kind of extended support for using this model, so your mileage may vary depending on use and context size.

  • (Nemo 12B instruct as base)
  • 200k randomized subset of GU_instruct-Remastered-1.1, with a splash of 25k hathor/poppy sauce, slow cooked for 3 epochs on medium heat.
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GGUF
Model size
12B params
Architecture
llama
Hardware compatibility
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