Triangle104/Lacaille-MoT-4B-Supreme2-Q4_K_S-GGUF

This model was converted to GGUF format from prithivMLmods/Lacaille-MoT-4B-Supreme2 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Lacaille-MoT-4B-Supreme2 is a high-efficiency, multi-domain model fine-tuned on Qwen3-4B using the Mixture of Thoughts (MoT) dataset enhanced with code, math, science expert clusters and an extended open code reasoning dataset. This model blends symbolic precision, scientific logic, and structured output fluency—making it an ideal tool for developers, educators, and researchers seeking advanced reasoning under constrained compute.

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Lacaille-MoT-4B-Supreme2-Q4_K_S-GGUF --hf-file lacaille-mot-4b-supreme2-q4_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Lacaille-MoT-4B-Supreme2-Q4_K_S-GGUF --hf-file lacaille-mot-4b-supreme2-q4_k_s.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Lacaille-MoT-4B-Supreme2-Q4_K_S-GGUF --hf-file lacaille-mot-4b-supreme2-q4_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Lacaille-MoT-4B-Supreme2-Q4_K_S-GGUF --hf-file lacaille-mot-4b-supreme2-q4_k_s.gguf -c 2048