Update README.md

README.md

@@ -1,4 +1,37 @@
 ---
 library_name: peft
 base_model: yanolja/EEVE-Korean-Instruct-10.8B-v1.0
----
+---
+
+``` python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import prepare_model_for_kbit_training, PeftModel, PeftConfig
+
+model_path = 'yanolja/EEVE-Korean-10.8B-v1.0'
+lora_path = './ALMA/checkpoint-378/adapter_model'
+
+bnb_config = BitsAndBytesConfig(load_in_4bit=True,bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.float16,)
+model = AutoModelForCausalLM.from_pretrained(model_path, quantization_config=bnb_config, trust_remote_code=True)
+model.config.use_cache = False
+model = PeftModel.from_pretrained(model, lora_path)
+model = prepare_model_for_kbit_training(model)
+tokenizer = AutoTokenizer.from_pretrained(model_path, padding_side='left')
+
+en_text = 'Hi.'
+ko_text = '안녕하세요.'
+en_prompt = f"이것을 영어에서 한국어로 번역하세요:\n영어: {en_text}\n한국어:"
+ko_prompt = f"Translate this from Korean to English:\nKorean: {ko_text}\nEnglish:"
+
+input_ids = tokenizer(en_prompt, return_tensors="pt", padding=True, max_length=256, truncation=True).input_ids.cuda()
+with torch.no_grad():
+    generated_ids = model.generate(input_ids=input_ids, num_beams=5, max_new_tokens=20, do_sample=True, temperature=0.6, top_p=0.9)
+outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+print(outputs)
+
+input_ids = tokenizer(ko_prompt, return_tensors="pt", padding=True, max_length=256, truncation=True).input_ids.cuda()
+with torch.no_grad():
+    generated_ids = model.generate(input_ids=input_ids, num_beams=5, max_new_tokens=20, do_sample=True, temperature=0.6, top_p=0.9)
+outputs = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+print(outputs)
+```