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---
language:
- ko
datasets:
- kyujinpy/ko-gu-platyorca-mergeset
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](https://huggingface.co/beomi/llama-2-koen-13b)
**Training Dataset**
- [kyujinpy/ko-gu-platyorca-mergeset](https://huggingface.co/datasets/kyujinpy/ko-gu-platyorca-mergeset).
---
# Model comparisons
> Ko-LLM leaderboard(11/27; [link](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard))
| 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-v4⭐ | 49.89 | 45.05 | **57.06** | 41.83 | **42.93** | 62.57 |
| **⭐My custom LLM 13B-v6⭐** | 47.78 | 45.22 | 56.60 | 42.36 | 42.31 | 52.42 |
---
# Model comparisons2
> AI-Harness evaluation; [link](https://github.com/Beomi/ko-lm-evaluation-harness)
| 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-v4⭐** | **0.7988** | 0.8279 | **0.4995** | 0.4953 | 0.3343 | 0.3558 | **0.7825** | 0.9698 |
| **⭐My custom LLM 13B-v6⭐** | 0.7938 | 0.8259 | 0.4905 | 0.5620 | **0.8656** | 0.8457 | 0.3720 | 0.9698 |
| [beomi/llama-2-koen-13b](https://huggingface.co/beomi/llama-2-koen-13b) | 0.7768 | 0.8128 | 0.4999 | 0.5127 | 0.3988 | 0.7038 | 0.5870 | 0.9748 |
---
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "PracticeLLM/Custom-KoLLM-13B-v6"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
``` |