metadata

license: llama3
library_name: transformers
pipeline_tag: text-generation
base_model: yentinglin/Llama-3-Taiwan-8B-Instruct-128k
language:
  - zh
  - en
tags:
  - zhtw
  - llama-cpp
  - gguf-my-repo
widget:
  - text: >-
      A chat between a curious user and an artificial intelligence assistant.
      The assistant gives helpful, detailed, and polite answers to the user's
      questions. USER: 你好，請問你可以幫我寫一封推薦信嗎？ ASSISTANT:

dollartw/Llama-3-Taiwan-8B-Instruct-128k-Q4_K_M-GGUF

This model was converted to GGUF format from yentinglin/Llama-3-Taiwan-8B-Instruct-128k 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 dollartw/Llama-3-Taiwan-8B-Instruct-128k-Q4_K_M-GGUF --hf-file llama-3-taiwan-8b-instruct-128k-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo dollartw/Llama-3-Taiwan-8B-Instruct-128k-Q4_K_M-GGUF --hf-file llama-3-taiwan-8b-instruct-128k-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 dollartw/Llama-3-Taiwan-8B-Instruct-128k-Q4_K_M-GGUF --hf-file llama-3-taiwan-8b-instruct-128k-q4_k_m.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo dollartw/Llama-3-Taiwan-8B-Instruct-128k-Q4_K_M-GGUF --hf-file llama-3-taiwan-8b-instruct-128k-q4_k_m.gguf -c 2048