base_model: IlyaGusev/saiga_gemma2_9b_abliterated_sft_m3_d9_abliterated
tags:
- llama-cpp
- gguf-my-repo
mrsobakin/saiga_gemma2_9b_abliterated_sft_m3_d9_abliterated-Q4_K_M-GGUF
This model was converted to GGUF format from IlyaGusev/saiga_gemma2_9b_abliterated_sft_m3_d9_abliterated 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)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo mrsobakin/saiga_gemma2_9b_abliterated_sft_m3_d9_abliterated-Q4_K_M-GGUF --hf-file saiga_gemma2_9b_abliterated_sft_m3_d9_abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo mrsobakin/saiga_gemma2_9b_abliterated_sft_m3_d9_abliterated-Q4_K_M-GGUF --hf-file saiga_gemma2_9b_abliterated_sft_m3_d9_abliterated-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 mrsobakin/saiga_gemma2_9b_abliterated_sft_m3_d9_abliterated-Q4_K_M-GGUF --hf-file saiga_gemma2_9b_abliterated_sft_m3_d9_abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo mrsobakin/saiga_gemma2_9b_abliterated_sft_m3_d9_abliterated-Q4_K_M-GGUF --hf-file saiga_gemma2_9b_abliterated_sft_m3_d9_abliterated-q4_k_m.gguf -c 2048