Update README.md
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README.md
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@@ -9,7 +9,7 @@ If no comment is made, None data regarding the product or shipping will be retur
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Used to quantization method like this ;
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import torch
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#quantizasyon yöntemi 4 bit hassasiyetiyle yapacak.
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quant_config = BitsAndBytesConfig(
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bnb_4bit_compute_dtype=torch.float16, #maliyeti de düşürmüş olduk, hızı da artırdık
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bnb_4bit_use_double_quant=True, # True model increases accuracy but causes it to work more costly.
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) #np4 saves more memory
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Here.. This code contains important steps to optimize LLM's memory usage and processing time.
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It is important to improve the performance and resource utilization of the model.
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#20GB model
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model = AutoModelForCausalLM.from_pretrained(
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pretrained_model_name_or_path=base_model, #used "meta-llama/Meta-Llama-3-8B-Instruct"
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@@ -34,4 +34,4 @@ model = AutoModelForCausalLM.from_pretrained(
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)
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model.config.use_cache = False
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model.config.pretraining_tp = 1
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-
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Used to quantization method like this ;
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+
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import torch
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#quantizasyon yöntemi 4 bit hassasiyetiyle yapacak.
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quant_config = BitsAndBytesConfig(
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bnb_4bit_compute_dtype=torch.float16, #maliyeti de düşürmüş olduk, hızı da artırdık
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bnb_4bit_use_double_quant=True, # True model increases accuracy but causes it to work more costly.
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) #np4 saves more memory
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+
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Here.. This code contains important steps to optimize LLM's memory usage and processing time.
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It is important to improve the performance and resource utilization of the model.
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+
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#20GB model
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model = AutoModelForCausalLM.from_pretrained(
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pretrained_model_name_or_path=base_model, #used "meta-llama/Meta-Llama-3-8B-Instruct"
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)
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model.config.use_cache = False
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model.config.pretraining_tp = 1
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