Triangle104/Omega-Qwen2.5-Coder-3B-Q4_K_M-GGUF

This model was converted to GGUF format from prithivMLmods/Omega-Qwen2.5-Coder-3B 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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Omega-Qwen2.5-Coder-3B is a compact and high-efficiency code-focused model fine-tuned on Qwen2.5-Coder-3B-Instruct, using the symbolic-rich Open-Omega-Forge-1M dataset. Designed specifically for hard-coded tasks and deterministic computation, this model runs in a "thinking-disabled" mode—delivering precise, structured outputs with minimal hallucination, making it ideal for rigorous coding workflows and embedded logic applications.

Thinking: Disabled

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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/Omega-Qwen2.5-Coder-3B-Q4_K_M-GGUF --hf-file omega-qwen2.5-coder-3b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Omega-Qwen2.5-Coder-3B-Q4_K_M-GGUF --hf-file omega-qwen2.5-coder-3b-q4_k_m.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/Omega-Qwen2.5-Coder-3B-Q4_K_M-GGUF --hf-file omega-qwen2.5-coder-3b-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Omega-Qwen2.5-Coder-3B-Q4_K_M-GGUF --hf-file omega-qwen2.5-coder-3b-q4_k_m.gguf -c 2048