Custom-KoLLM-13B-v8 / README.md
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metadata
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

Training Dataset


Model comparisons

Ko-LLM leaderboard(11/27; 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-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

### 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