Model Card for Model ID
base_model : google/gemma-7b-it
Basic usage
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("MDDDDR/gemma-7b-it-v0.1")
model = AutoModelForCausalLM.from_pretrained(
"MDDDDR/gemma-7b-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 : sean0042/KorMedMCQA
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 = 32,
lora_alpha = 32,
lora_dropout = 0.05,
target_modules = ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']
)
Hardware
A100 40GB x 1