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Update README.md

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  1. README.md +7 -1
README.md CHANGED
@@ -25,16 +25,21 @@ This model has been fine tuned with mosaicml/instruct-v3 dataset with 2 epoch on
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  ## How to use?
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  from peft import PeftModel
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- # load the base model
 
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  model_path = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
 
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  tokenizer=AutoTokenizer.from_pretrained(model_path)
 
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  model = AutoModelForCausalLM.from_pretrained(
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  model_path,
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  torch_dtype = torch.bfloat16,
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  device_map = "auto",
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  trust_remote_code = True
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  )
 
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  #load the adapter
 
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  model_peft = PeftModel.from_pretrained(model, "azam25/TinyLlama_instruct_generation")
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  messages = [{
@@ -46,6 +51,7 @@ messages = [{
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  }]
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  def generate_response(message, model):
 
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  prompt = tokenizer.apply_chat_template(messages, tokenize=False)
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  encoded_input = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
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  model_inputs = encoded_input.to('cuda')
 
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  ## How to use?
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  from peft import PeftModel
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+ #load the base model
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+
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  model_path = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
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+
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  tokenizer=AutoTokenizer.from_pretrained(model_path)
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+
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  model = AutoModelForCausalLM.from_pretrained(
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  model_path,
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  torch_dtype = torch.bfloat16,
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  device_map = "auto",
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  trust_remote_code = True
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  )
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+
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  #load the adapter
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+
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  model_peft = PeftModel.from_pretrained(model, "azam25/TinyLlama_instruct_generation")
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  messages = [{
 
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  }]
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  def generate_response(message, model):
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+
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  prompt = tokenizer.apply_chat_template(messages, tokenize=False)
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  encoded_input = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
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  model_inputs = encoded_input.to('cuda')