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-v0.1")
model = AutoModelForCausalLM.from_pretrained(
"MDDDDR/Ko-Luxia-8B-it-v0.1",
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
lora_config and bnb_config in Training
bnd_config = BitsAndBytesConfig(
load_in_4bit = True,
bnb_4bit_use_double_quant = True,
bnb_4bit_quant_type = 'nf4',
bnb_4bit_compute_dtype = torch.bfloat16
)
lora_config = LoraConfig(
r = 16,
lora_alpha = 16,
lora_dropout = 0.05,
target_modules = ['gate_proj', 'up_proj', 'down_proj']
)
Hardware
RTX 3090 Ti 24GB x 1
Evaluation Benchmark Results
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | ||
|---|---|---|---|---|---|---|---|---|
| kobest_boolq | 1 | none | 0 | acc | ↑ | 0.6425 | ± | 0.0128 |
| none | 0 | f1 | ↑ | 0.6054 | ± | N/A | ||
| kobest_copa | 1 | none | 0 | acc | ↑ | 0.7340 | ± | 0.0140 |
| none | 0 | f1 | ↑ | 0.7333 | ± | N/A | ||
| kobest_hellaswag | 1 | none | 0 | acc | ↑ | 0.4760 | ± | 0.0224 |
| none | 0 | acc_norm | ↑ | 0.6120 | ± | 0.0218 | ||
| none | 0 | f1 | ↑ | 0.4745 | ± | N/A | ||
| kobest_sentineg | 1 | none | 0 | acc | ↑ | 0.5894 | ± | 0.0247 |
| none | 0 | f1 | ↑ | 0.5682 | ± | N/A |