metadata

base_model: IlyaGusev/saiga_llama3_8b_sft_m11_d7_abliterated_kto_m7_d7
tags:
  - llama-cpp
  - gguf-my-repo

VlSav/saiga_llama3_8b_sft_m11_d7_abliterated_kto_m7_d7-Q6_K-GGUF

This model was converted to GGUF format from IlyaGusev/saiga_llama3_8b_sft_m11_d7_abliterated_kto_m7_d7 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 VlSav/saiga_llama3_8b_sft_m11_d7_abliterated_kto_m7_d7-Q6_K-GGUF --hf-file saiga_llama3_8b_sft_m11_d7_abliterated_kto_m7_d7-q6_k.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo VlSav/saiga_llama3_8b_sft_m11_d7_abliterated_kto_m7_d7-Q6_K-GGUF --hf-file saiga_llama3_8b_sft_m11_d7_abliterated_kto_m7_d7-q6_k.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 VlSav/saiga_llama3_8b_sft_m11_d7_abliterated_kto_m7_d7-Q6_K-GGUF --hf-file saiga_llama3_8b_sft_m11_d7_abliterated_kto_m7_d7-q6_k.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo VlSav/saiga_llama3_8b_sft_m11_d7_abliterated_kto_m7_d7-Q6_K-GGUF --hf-file saiga_llama3_8b_sft_m11_d7_abliterated_kto_m7_d7-q6_k.gguf -c 2048