--- datasets: - kyujinpy/KOpen-platypus language: - ko - en pipeline_tag: text-generation --- ### Model Card for Model ID base_model : [Ko-Llama3-Luxia-8B](https://huggingface.co./saltlux/Ko-Llama3-Luxia-8B) ### Basic usage ```python # 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](https://huggingface.co./datasets/kyujinpy/KOpen-platypus) ### lora_config and bnb_config in Training ```python 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 |