README.md

metadata

language:
  - ko
datasets:
  - kyujinpy/Ko-various-dataset
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/Ko-various-dataset.

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Model comparisons

Ko-LLM leaderboard(11/27; link)

Model	Average	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2
⭐My custom LLM 13B-v1⭐	50.19	45.99	56.93	41.78	41.66	64.58
⭐My custom LLM 13B-v2⭐	48.28	45.73	56.97	38.77	38.75	61.16
⭐My custom LLM 13B-v3⭐	46.40	44.71	56.89	40.86	44.22	45.34

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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-v1⭐	0.7987	0.8269	0.4994	0.5660	0.3343	0.5060	0.6984	0.9723
⭐My custom LLM 13B-v2⭐	0.7938	0.8209	0.4978	0.4893	0.3343	0.5614	0.6283	0.9773
⭐My custom LLM 13B-v3⭐	0.8107	0.8359	0.5176	0.5182	0.6702	0.7851	0.5241	0.9698
beomi/llama-2-koen-13b	0.7768	0.8128	0.4999	0.5127	0.3988	0.7038	0.5870	0.9748

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Implementation Code

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

repo = "PracticeLLM/Custom-KoLLM-13B-v3"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)

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