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---
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](https://github.com/KyujinHan/KoT-platypus)
**Base Model**
[KO-Platypus2-7B-ex](https://huggingface.co/kyujinpy/KO-Platypus2-7B-ex)
More detail repo(Github): [CoT-llama2](https://github.com/Marker-Inc-Korea/CoT-llama2)
More detail repo(Github): [KO-Platypus2](https://github.com/Marker-Inc-Korea/KO-Platypus)
**Training Dataset**
I use [KoCoT_2000](https://huggingface.co/datasets/kyujinpy/KoCoT_2000).
Using DeepL, translate about [kaist-CoT](https://huggingface.co/datasets/kaist-ai/CoT-Collection).
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](https://huggingface.co/spaces/upstage/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 |
| [KO-Platypus2-13B](https://huggingface.co/kyujinpy/KO-Platypus2-13B) | 47.90 | 44.20 | 54.31 | 42.47 | 44.41 | 54.11 |
| [hyunseoki/ko-en-llama2-13b](https://huggingface.co/hyunseoki/ko-en-llama2-13b) | 46.68 | 42.15 | 54.23 | 38.90 | 40.74 | 57.39 |
| [MarkrAI/kyujin-CoTy-platypus-ko-12.8b](https://huggingface.co/MarkrAI/kyujin-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](https://huggingface.co/momo/polyglot-ko-12.8b-Chat-QLoRA-Merge) | 45.71 | 35.49 | 49.93 | 25.97 | 39.43 | 77.70 |
> Compare with Top 4 SOTA models. (update: 10/07)
# Implementation Code
```python
### 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](https://huggingface.co/beomi/llama-2-ko-7b)
--- |