Create README.md

README.md

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+---
+pipeline_tag: multilabel-classification
+---
+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
+>>