metadata
language:
- ko
- en
pipeline_tag: text-generation
datasets:
- DILAB-HYU/KoQuality
Model Card for Model ID
base_model : meta-llama/Meta-Llama-3.1-8B-Instruct
Basic usage
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("MDDDDR/Meta-Llama-3.1-8B-it-v0.1")
model = AutoModelForCausalLM.from_pretrained(
"MDDDDR/Meta-Llama-3.1-8B-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_1 : DILAB-HYU/KoQuality
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 = 8,
lora_alpha = 8,
lora_dropout = 0.05,
target_modules = ['gate_proj', 'up_proj', 'down_proj']
)
Model evaluation
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | ||
|---|---|---|---|---|---|---|---|---|
| kobest_boolq | 1 | none | 0 | acc | ↑ | 0.5150 | ± | 0.0133 |
| none | 0 | f1 | ↑ | 0.3634 | ± | N/A | ||
| kobest_copa | 1 | none | 0 | acc | ↑ | 0.6280 | ± | 0.0153 |
| none | 0 | f1 | ↑ | 0.6279 | ± | N/A | ||
| kobest_hellaswag | 1 | none | 0 | acc | ↑ | 0.4280 | ± | 0.0221 |
| none | 0 | acc_norm | ↑ | 0.5540 | ± | 0.0223 | ||
| none | 0 | f1 | ↑ | 0.4250 | ± | N/A | ||
| kobest_sentineg | 1 | none | 0 | acc | ↑ | 0.7406 | ± | 0.0220 |
| none | 0 | f1 | ↑ | 0.7317 | ± | N/A |
Hardware
- RTX 3090 Ti 24GB x 1
- Training Time : 1 hours