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 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | NaN | 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)