---
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](https://huggingface.co./beomi/llama-2-koen-13b)   

**Training Dataset**   
- [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co./datasets/kyujinpy/KOR-OpenOrca-Platypus-v3).  

---  
# 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-v2⭐ | 48.28 | 45.73 | 56.97 | 38.77 | 38.75 | 61.16 | 
| **⭐My custom LLM 13B-v4⭐** | 49.89 | 45.05 | **57.06** | **41.83** | **42.93** | 62.57 | 

---  
# 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-v2⭐ | 0.7938 | 0.8209 | 0.4978 | 0.4893 | **0.3343** | 0.5614 | 0.6283 | 0.9773 |
| **⭐My custom LLM 13B-v4⭐** | **0.7988** | 0.8279 | **0.4995** | 0.4953 | **0.3343** | 0.3558 | **0.7825** | 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-v4"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```
  
---
# Hyperparameters
- learning_rate: 4e-4
- batch_size: 16
- epoch: 1
- lora_target_modules: [gate_proj, down_proj, up_proj, q_proj, k_proj, v_proj, o_proj]
- cutoff_len: 4096