Update README.md

README.md

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-pipeline_tag: multilabel-classification
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-Multi Label Classification 
 
-Description: Labeling the product comments of the e-commerce data we have created according to the labels we have specified. 
-It returns us a positive or negative value based on the comments made on the product or shipping. 
-If no comment is made, None data regarding the product or shipping will be returned. In this project I used the model "meta-llama/Meta-Llama-3-8B-Instruct" 
 
-Used to quantization method like this ;
-
-
-import torch
-#quantizasyon yöntemi 4 bit hassasiyetiyle yapacak.
-quant_config = BitsAndBytesConfig(
-    load_in_4bit=True, #olabildiğince küçültmeye çalışıyoruz maliyet açısından, ama doğruluğu da düşüyor
-    bnb_4bit_quant_type="nf4", #"fp4" , nf4 daha az yer kaplar daha hızlıdır ama doğruluğu kıyasla daha az
-    bnb_4bit_compute_dtype=torch.float16, #maliyeti de düşürmüş olduk, hızı da artırdık
-    bnb_4bit_use_double_quant=True, # True model increases accuracy but causes it to work more costly.
-) #np4 saves more memory
-
-
-
-Here.. This code contains important steps to optimize LLM's memory usage and processing time. 
-It is important to improve the performance and resource utilization of the model.
-
-#20GB model
-model = AutoModelForCausalLM.from_pretrained(
-                                              pretrained_model_name_or_path=base_model, #used "meta-llama/Meta-Llama-3-8B-Instruct" 
-                                              quantization_config=quant_config,
-                                              device_map={"": 0}, 
-                                              #use_auth_token=True
-
-                                              )
-model.config.use_cache = False 
-model.config.pretraining_tp = 1