Triangle104/Rombos-Coder-V2.5-Qwen-14b-Q4_K_M-GGUF

This model was converted to GGUF format from rombodawg/Rombos-Coder-V2.5-Qwen-14b 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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Model details:

Rombos-Coder-V2.5-Qwen-14b is a continues finetuned version of Qwen2.5-Coder-14B-Instruct. I took it upon myself to merge the instruct model with the base model myself using the Ties merge method as demonstrated in my own "Continuous Finetuning" method (Linked bellow).

https://docs.google.com/document/d/1OjbjU5AOz4Ftn9xHQrX3oFQGhQ6RDUuXQipnQ9gn6tU/edit?usp=sharing

This version of the model shows higher performance than the original instruct and base models.

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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/Rombos-Coder-V2.5-Qwen-14b-Q4_K_M-GGUF --hf-file rombos-coder-v2.5-qwen-14b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Rombos-Coder-V2.5-Qwen-14b-Q4_K_M-GGUF --hf-file rombos-coder-v2.5-qwen-14b-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/Rombos-Coder-V2.5-Qwen-14b-Q4_K_M-GGUF --hf-file rombos-coder-v2.5-qwen-14b-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Rombos-Coder-V2.5-Qwen-14b-Q4_K_M-GGUF --hf-file rombos-coder-v2.5-qwen-14b-q4_k_m.gguf -c 2048