Update README.md

README.md

@@ -61,13 +61,52 @@ tags:
 
 # ❺ 사용 방법
   <pre><code>
-  from transformers import AutoModel, AutoTokenizer
+  import os
+  import os.path as osp
+  import sys
+  import fire
+  import json
+  from typing import List, Union
+  import pandas as pd
+  import torch
+  from torch.nn import functional as F
   
-  tokenizer = AutoTokenizer.from_pretrained("")
-  model = AutoModel.from_pretrained("")
+  import transformers
+  from transformers import TrainerCallback, TrainingArguments, TrainerState, TrainerControl, BitsAndBytesConfig
+  from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
+  from transformers import LlamaForCausalLM, LlamaTokenizer
+  from transformers import AutoModelForCausalLM, AutoTokenizer
+  
+  from datasets import load_dataset
+  
+  from peft import (
+      LoraConfig,
+      get_peft_model,
+      set_peft_model_state_dict
+  )
+  from peft import PeftModel
+  import re
+  import ast
+  
+  device = 'auto' #@param {type: "string"}
+  model = '' #@param {type: "string"}
+  model = AutoModelForCausalLM.from_pretrained(
+    model,
+    quantization_config=bnb_config,
+    #load_in_4bit=True, # Quantization Load
+    device_map=device)
+
+  tokenizer = AutoTokenizer.from_pretrained(base_LLM_model)
+
+  input_text = "안녕하세요."
+  inputs = tokenizer(input_text, return_tensors="pt")
+  inputs = inputs.to("cuda:0")
+  
+  with torch.no_grad():
+      outputs = model.generate(**inputs, max_length=1024)
+  
+  result = tokenizer.decode(outputs[0], skip_special_tokens=True)
   
-  inputs = tokenizer("안녕하세요", return_tensors="pt")
-  outputs = model(**inputs)
 </code></pre>
 
 ## ✅ ktds는 openchat 외에도 LlaMA, Polyglot, EEVE 등 대표적인 LLM에 다양한 영역의 한국의 문화와 지식을 파인튜닝한 LLM을 제공할 예정입니다.
@@ -130,13 +169,51 @@ may produce biased responses if trained on biased data.
 
 # ❺ Usage Instructions
 <pre><code>
-  from transformers import AutoModel, AutoTokenizer
+  import os
+  import os.path as osp
+  import sys
+  import fire
+  import json
+  from typing import List, Union
+  import pandas as pd
+  import torch
+  from torch.nn import functional as F
+  
+  import transformers
+  from transformers import TrainerCallback, TrainingArguments, TrainerState, TrainerControl, BitsAndBytesConfig
+  from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
+  from transformers import LlamaForCausalLM, LlamaTokenizer
+  from transformers import AutoModelForCausalLM, AutoTokenizer
+  
+  from datasets import load_dataset
+  
+  from peft import (
+      LoraConfig,
+      get_peft_model,
+      set_peft_model_state_dict
+  )
+  from peft import PeftModel
+  import re
+  import ast
+  
+  device = 'auto' #@param {type: "string"}
+  model = '' #@param {type: "string"}
+  model = AutoModelForCausalLM.from_pretrained(
+    model,
+    quantization_config=bnb_config,
+    #load_in_4bit=True, # Quantization Load
+    device_map=device)
+
+  tokenizer = AutoTokenizer.from_pretrained(base_LLM_model)
+
+  input_text = "안녕하세요."
+  inputs = tokenizer(input_text, return_tensors="pt")
+  inputs = inputs.to("cuda:0")
   
-  tokenizer = AutoTokenizer.from_pretrained("")
-  model = AutoModel.from_pretrained("")
+  with torch.no_grad():
+      outputs = model.generate(**inputs, max_length=1024)
   
-  inputs = tokenizer("Hello?", return_tensors="pt")
-  outputs = model(**inputs)
+  result = tokenizer.decode(outputs[0], skip_special_tokens=True)
 </code></pre>
 
 ## KTDS plans to provide fine-tuned LLMs (Large Language Models) across various domains of Korean culture and knowledge,