metadata

license: gemma
tags:
  - alignment-handbook
  - trl
  - sft
  - generated_from_trainer
  - llama-cpp
base_model: masakhane/African-ultrachat-alpaca
datasets:
  - masakhane/african-ultrachat
  - untrachat_en
  - sd
model-index:
  - name: zephyr-7b-gemma-sft-african-ultraalpaca
    results: []
language:
  - sw
  - yo
  - am
  - ha
  - ig
  - rw
  - st
  - sn
  - so
  - zu
library_name: adapter-transformers

Quantized African-ultrachat-alpaca-Q5_K_M-GGUF

This model was converted to GGUF format from masakhane/African-ultrachat-alpaca using llama.cpp via the ggml.ai's GGUF-my-repo space.

Refer to the original model card for more details on the model.

Infererence : Token Speed: 13.72t/s

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 --hf-repo Svngoku/African-ultrachat-alpaca-Q5_K_M-GGUF --hf-file african-ultrachat-alpaca-q5_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Svngoku/African-ultrachat-alpaca-Q5_K_M-GGUF --hf-file african-ultrachat-alpaca-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.

./main --hf-repo Svngoku/African-ultrachat-alpaca-Q5_K_M-GGUF --hf-file african-ultrachat-alpaca-q5_k_m.gguf -p "The meaning to life and the universe is"

./server --hf-repo Svngoku/African-ultrachat-alpaca-Q5_K_M-GGUF --hf-file african-ultrachat-alpaca-q5_k_m.gguf -c 2048