metadata

language:
  - ko
datasets:
  - kyujinpy/KOR-OpenOrca-Platypus-v3
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-sa-4.0

⭐My custom LLM 13B⭐

Model Details

Model Developers

Kyujin Han (kyujinpy)

Model Architecture

My custom LLM 13B is an auto-regressive language model based on the LLaMA2 transformer architecture.

Base Model

beomi/llama-2-koen-13b

Training Dataset

kyujinpy/KOR-OpenOrca-Platypus-v3.

Model comparisons1

Ko-LLM leaderboard(11/23; link)

Model	Average	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2
⭐My custom LLM 13B⭐	50.19	45.99	56.93	41.78	41.66	64.58

Model comparisons2

AI-Harness evaluation; link

Model	Copa	Copa	HellaSwag	HellaSwag	BoolQ	BoolQ	Sentineg	Sentineg
	0-shot	5-shot	0-shot	5-shot	0-shot	5-shot	0-shot	5-shot
⭐My custom LLM 13B⭐	NaN	0.8269	NaN	0.5660	NaN	0.5060	NaN	0.9723
beomi/llama-2-koen-13b	0.7768	0.8128	0.4999	0.5127	0.3988	0.7038	0.5870	0.9748

Implementation Code

### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "PracticeLLM/Custom-KoLLM-13B-v1"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)