README.md

---
language:
- ko
datasets: 
- kyujinpy/OpenOrca-ko-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/OpenOrca-ko-v3](https://huggingface.co/datasets/kyujinpy/OpenOrca-ko-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-v4⭐ | 49.89 | 45.05 | **57.06** | **41.83** | **42.93** | 62.57 | 
| **⭐My custom LLM 13B-v8⭐** | 49.84 | 45.65 | 56.98 | 41.37 | 41.42 | 59.50 | 
  
--- 
# Implementation Code
```python
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "PracticeLLM/Custom-KoLLM-13B-v8"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
```

# Hyperparameters
- QLoRA  
- lora_target_modules '[gate_proj, down_proj, up_proj]'  
- lora_r 64