ThijsL202/EVA-Hamanasu-Magnum-QwQ-32B-GGUF

This model was converted to GGUF format from ThijsL202/EVA-Hamanasu-Magnum-QwQ-32B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

I created it because I like win10/EVA-QwQ-32B-Preview a lot, sure, it can be repetitive and it's not really affected by the sampler settings when using the default koboldcpp order, but it's still one of the best for the way I roleplay.

I might try this again using different models (Instead of EVA-UNIT-01/EVA-Qwen2.5-32B-v0.2) with the same merge method, any recommendations? Feel free to ask me.

Available Quantizations

• Q4_K_M, Q5_K_M, Q6_K, Q8_0

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 ThijsL202/EVA-Hamanasu-Magnum-QwQ-32B-GGUF --hf-file eva-hamanasu-magnum-qwq-32b-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo ThijsL202/EVA-Hamanasu-Magnum-QwQ-32B-GGUF --hf-file eva-hamanasu-magnum-qwq-32b-q5_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 ThijsL202/EVA-Hamanasu-Magnum-QwQ-32B-GGUF --hf-file eva-hamanasu-magnum-qwq-32b-q5_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo ThijsL202/EVA-Hamanasu-Magnum-QwQ-32B-GGUF --hf-file eva-hamanasu-magnum-qwq-32b-q5_k_m.gguf -c 2048