metadata
license: apache-2.0
license_link: https://huggingface.co/skt/A.X-4.0-Light/blob/main/LICENSE
language:
- en
- ko
pipeline_tag: text-generation
library_name: transformers
model_id: skt/A.X-4.0-Light
developers: SKT AI Model Lab
tags:
- llama-cpp
- gguf-my-repo
base_model: skt/A.X-4.0-Light
model-index:
- name: A.X-4.0-Light
results:
- task:
type: generate_until
name: mmlu
dataset:
name: mmlu (chat CoT)
type: hails/mmlu_no_train
metrics:
- type: exact_match
value: 75.43
name: exact_match
- task:
type: generate_until
name: kmmlu
dataset:
name: kmmlu (chat CoT)
type: HAERAE-HUB/KMMLU
metrics:
- type: exact_match
value: 64.15
name: exact_match
Junheelee070712/A.X-4.0-Light-Q4_K_M-GGUF
This model was converted to GGUF format from skt/A.X-4.0-Light 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 Junheelee070712/A.X-4.0-Light-Q4_K_M-GGUF --hf-file a.x-4.0-light-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Junheelee070712/A.X-4.0-Light-Q4_K_M-GGUF --hf-file a.x-4.0-light-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 Junheelee070712/A.X-4.0-Light-Q4_K_M-GGUF --hf-file a.x-4.0-light-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Junheelee070712/A.X-4.0-Light-Q4_K_M-GGUF --hf-file a.x-4.0-light-q4_k_m.gguf -c 2048