⭐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
Training Dataset
Model comparisons
Ko-LLM leaderboard(11/25; link)
| 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 |
Model comparisons2
AI-Harness evaluation; link
| 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 |
| beomi/llama-2-koen-13b | 0.7768 | 0.8128 | 0.4999 | 0.5127 | 0.3988 | 0.7038 | 0.5870 | 0.9748 |
Implementation Code
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "PracticeLLM/Custom-KoLLM-13B-v2"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)