CoolFace
Modelpublic

IcosaComputingHF/mert-fusion

sourceHugging Faceupdated 9mo agoView on Hugging Face
0likes22downloads
Model Card

berkesencan/mert-icosacomputing.coma9e6c07a-7d87-47c3-aa49-803ca7ed0dc2HF-Q4KM-GGUF

This model was converted to GGUF format from `IcosaComputingHF/mert-icosacomputing.com_a9e6c07a-7d87-47c3-aa49-803ca7ed0dc2_HF` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

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

bash
brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

bash
llama-cli --hf-repo berkesencan/mert-icosacomputing.com_a9e6c07a-7d87-47c3-aa49-803ca7ed0dc2_HF-Q4_K_M-GGUF --hf-file mert-icosacomputing.com_a9e6c07a-7d87-47c3-aa49-803ca7ed0dc2_hf-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

bash
llama-server --hf-repo berkesencan/mert-icosacomputing.com_a9e6c07a-7d87-47c3-aa49-803ca7ed0dc2_HF-Q4_K_M-GGUF --hf-file mert-icosacomputing.com_a9e6c07a-7d87-47c3-aa49-803ca7ed0dc2_hf-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 berkesencan/mert-icosacomputing.com_a9e6c07a-7d87-47c3-aa49-803ca7ed0dc2_HF-Q4_K_M-GGUF --hf-file mert-icosacomputing.com_a9e6c07a-7d87-47c3-aa49-803ca7ed0dc2_hf-q4_k_m.gguf -p "The meaning to life and the universe is"

or 

./llama-server --hf-repo berkesencan/mert-icosacomputing.com_a9e6c07a-7d87-47c3-aa49-803ca7ed0dc2_HF-Q4_K_M-GGUF --hf-file mert-icosacomputing.com_a9e6c07a-7d87-47c3-aa49-803ca7ed0dc2_hf-q4_k_m.gguf -c 2048