metadata

language:
  - ko
datasets:
  - kyujinpy/KoCoT_2000
library_name: transformers
pipeline_tag: text-generation
license: cc-by-nc-4.0

KoT-platypus2

CoT + KO-platypus2 = KoT-platypus2

Model Details

Model Developers Kyujin Han (kyujinpy)

Input Models input text only.

Output Models generate text only.

Model Architecture
KoT-platypus2-13B is an auto-regressive language model based on the LLaMA2 transformer architecture.

Repo Link
Github KoT-platypus: KoT-platypus2

Base Model
KO-Platypus2-7B-ex
More detail repo(Github): CoT-llama2
More detail repo(Github): KO-Platypus2

Training Dataset
I use KoCoT_2000.
Using DeepL, translate about kaist-CoT.

I use A100 GPU 40GB and COLAB, when trianing.

Training Hyperparameters

Hyperparameters	Value
batch_size	`64`
micro_batch_size	`1`
Epochs	`15`
learning_rate	`1e-5`
cutoff_len	`4096`
lr_scheduler	`linear`
base_model	`kyujinpy/KO-Platypus2-13B`

Model Benchmark

KO-LLM leaderboard

Follow up as Open KO-LLM LeaderBoard.

Model	Average	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2
KoT-Platypus2-13B(ours)	NaN	NaN	NaN	NaN	NaN	NaN
hyunseoki/ko-en-llama2-13b	46.68	42.15	54.23	38.90	40.74	57.39
CoTy-platypus-ko-12.8b	46.44	34.98	49.11	25.68	37.59	84.86
momo/polyglot-ko-12.8b-Chat-QLoRA-Merge	45.71	35.49	49.93	25.97	39.43	77.70
KoT-platypus2-7B	45.62	38.05	49.63	34.68	37.69	68.08

Compare with Top 4 SOTA models. (update: 10/05)

Implementation Code

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

repo = "kyujinpy/KoT-platypus2-13B"
CoT-llama = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
CoT-llama_tokenizer = AutoTokenizer.from_pretrained(repo)

Readme format: beomi/llama-2-ko-7b