metadata

library_name: transformers
license: apache-2.0
basemodel: meta-llama/Meta-Llama-3-8B-Instruct
datasets:
  - Saxo/total_ko_train_set_1_without_wiki_with_orca
language:
  - ko
  - en
pipeline_tag: text-generation

Model Card for Model ID

AI 와 빅데이터 분석 전문 기업인 Linkbricks의 데이터사이언티스트인 지윤성 박사(Saxo)가 meta-llama/Meta-Llama-3-8B를 베이스모델로 GCP상의 H100-80G 8개를 통해 SFT-DPO 훈련을 한(8000 Tokens) 한글 기반 모델. 토크나이저는 라마3랑 동일하며 한글 VOCA 확장은 하지 않은 버전 입니다.

Dr. Yunsung Ji (Saxo), a data scientist at Linkbricks, a company specializing in AI and big data analytics, trained the meta-llama/Meta-Llama-3-8B base model on 8 H100-60Gs on GCP for 4 hours of instructional training (8000 Tokens). Accelerate, Deepspeed Zero-3 libraries were used.

www.linkbricks.com, www.linkbricks.vc

Configuration including BitsandBytes

bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_use_double_quant=False, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch_dtype )

args = TrainingArguments( output_dir=project_name, run_name=run_name_str, overwrite_output_dir=True, num_train_epochs=20, per_device_train_batch_size=1, gradient_accumulation_steps=4, #1 gradient_checkpointing=True, optim="paged_adamw_32bit", #optim="adamw_8bit", logging_steps=10, save_steps=100, save_strategy="epoch", learning_rate=2e-4, #2e-4 weight_decay=0.01, max_grad_norm=1, #0.3 max_steps=-1, warmup_ratio=0.1, group_by_length=False, fp16 = not torch.cuda.is_bf16_supported(), bf16 = torch.cuda.is_bf16_supported(), #fp16 = True, lr_scheduler_type="cosine", #"constant", disable_tqdm=False, report_to='wandb', push_to_hub=False )