Create ReadMe.md

README.md

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+---
+license: apache-2.0
+datasets:
+- aswin1906/llama2-sql-instruct-2k
+language:
+- en
+pipeline_tag: question-answering
+tags:
+- code
+---
+
+
+# Fine-Tune Llama 2 Model Using qLORA for Custom SQL Dataset
+Instruction fine-tuning has become extremely popular since the (accidental) release of LLaMA. 
+The size of these models and the peculiarities of training them on instructions and answers introduce more complexity and often require parameter-efficient learning techniques such as QLoRA.
+Refer Dataset at **aswin1906/llama2-sql-instruct-2k**
+## Model Background
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65059e484a8839a8bd5f67cb/tkHJ3Tuh7Jim4jKg6_h_m.png)
+
+## Model Inference
+Refer the below code to apply model inference
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import torch, re
+from rich import print
+
+class Training:
+    def __init__(self) -> None:
+        self.model_name= "meta-llama/Llama-2-7b-chat-hf"
+        self.dataset= "aswin1906/llama2-sql-instruct-2k"
+        self.model_path= "aswin1906/llama-7b-sql-2k"
+        self.instruction= 'You are given the following SQL table structure described by CREATE TABLE statement: CREATE TABLE "l" ( "player" text, "no" text, "nationality" text, "position" text, "years_in_toronto" text, "school_club_team" text ); Write an SQL query that provides the solution to the following question: '
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.model_path,
+            load_in_8bit=False,
+            torch_dtype=torch.float16,
+            device_map="auto"
+        )
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_path)
+    
+    def inference(self, prompt):
+        """
+        Prompting started here
+        """        
+        # Run text generation pipeline with our next model
+        pipe = pipeline(task="text-generation", model=self.model, tokenizer=self.tokenizer, max_length=200)
+        result = pipe(f'<s>[INST] {self.instruction}"{prompt}". [/INST]')
+        response= result[0]['generated_text'].split('[/INST]')[-1]
+        return response
+    
+train= Training()
+instruction= re.split(';|by CREATE', train.instruction)
+print(f"[purple4] ------------------------------Instruction--------------------------")
+print(f"[medium_spring_green] {instruction[0]}")
+print(f"[bold green]CREATE{instruction[1]};")
+print(f"[medium_spring_green] {instruction[2]}")
+print(f"[purple4] -------------------------------------------------------------------")
+while True:
+    # prompt = 'What position does the player who played for butler cc (ks) play?'
+    print("[bold blue]#Human: [bold green]", end="")
+    user = input()
+    print('[bold blue]#Response: [bold green]', train.inference(user))
+
+```
+
+Contact **aswin1906@gmail.com** for model training code
+
+## output
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65059e484a8839a8bd5f67cb/ny_K7xBp53FILIhJkieX5.png)
+