metadata
license: other
license_name: qwen
language:
- th
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- openthaigpt
- qwen
- llama-cpp
- gguf-my-repo
base_model: openthaigpt/openthaigpt1.5-7b-instruct
model-index:
- name: OpenThaiGPT1.5-7b
results:
- task:
type: text-generation
dataset:
name: ThaiExam
type: multiple_choices
metrics:
- type: accuracy
value: 52.04
name: Thai Exam(Acc)
- type: Accuracy
value: 54.01
name: M3Exam(Acc)
source:
url: https://huggingface.co/spaces/ThaiLLM-Leaderboard/leaderboard
name: ๐น๐ญ Thai LLM Leaderboard
Dev-p2om/openthaigpt1.5-7b-instruct-Q4_K_M-GGUF
This model was converted to GGUF format from openthaigpt/openthaigpt1.5-7b-instruct
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 Dev-p2om/openthaigpt1.5-7b-instruct-Q4_K_M-GGUF --hf-file openthaigpt1.5-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo Dev-p2om/openthaigpt1.5-7b-instruct-Q4_K_M-GGUF --hf-file openthaigpt1.5-7b-instruct-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 Dev-p2om/openthaigpt1.5-7b-instruct-Q4_K_M-GGUF --hf-file openthaigpt1.5-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo Dev-p2om/openthaigpt1.5-7b-instruct-Q4_K_M-GGUF --hf-file openthaigpt1.5-7b-instruct-q4_k_m.gguf -c 2048