README.md

metadata

datasets:
  - kyujinpy/KOpen-platypus
language:
  - ko
  - en
pipeline_tag: text-generation

Model Card for Model ID

base_model : Ko-Llama3-Luxia-8B

Basic usage

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("MDDDDR/Ko-Luxia-8B-it-v0.2")
model = AutoModelForCausalLM.from_pretrained(
    "MDDDDR/Ko-Luxia-8B-it-v0.2",
    device_map="auto",
    torch_dtype=torch.bfloat16
)

input_text = "사과가 뭐야?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")

outputs = model.generate(**input_ids)
print(tokenizer.decode(outputs[0]))

Training dataset

dataset : kyujinpy/KOpen-platypus

bnd_config = BitsAndBytesConfig( load_in_4bit = True )

lora_config = LoraConfig( r = 16, lora_alpha = 16, lora_dropout = 0.05, target_modules = ['down_proj', 'up_proj', 'gate_proj'] )

Hardware

RTX 3090 Ti 24GB x 1

Model Benchmark Results

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
kobest_boolq	1	none	0	acc	↑	0.5278	±	0.0133
		none	0	f1	↑	0.3954	±	N/A
kobest_copa	1	none	0	acc	↑	0.7380	±	0.0139
		none	0	f1	↑	0.7372	±	N/A
kobest_hellaswag	1	none	0	acc	↑	0.4800	±	0.0224
		none	0	acc_norm	↑	0.6180	±	0.0218
		none	0	f1	↑	0.4774	±	N/A
kobest_sentineg	1	none	0	acc	↑	0.5390	±	0.0250
		none	0	f1	↑	0.5037	±	N/A