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---
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 |