Update README.md

README.md

@@ -54,6 +54,111 @@ Fine-tuning focused on enhancing domain-specific knowledge using a dataset curat
 
 ## 📦 How to Use
 
+
+1. **Install Dependencies**:
+  ```python
+  %%capture
+  !pip install unsloth
+
+  !pip uninstall unsloth -y && pip install --upgrade --no-cache-dir --no-deps git+https://github.com/unslothai/unsloth.git
+  ```
+2. **Load the model**:
+
+  ```python
+  metallurgy_prompt = """You are a highly knowledgeable assistant specializing in metallurgy, materials science,
+  and engineering. Below is a technical instruction.Your task is to provide an accurate, domain-specific response that appropriately addresses the request.
+  Ensure Your response is detailed,Provide scientifically rigorous and quantitative responses,Reference fundamental principles and mechanisms,
+  Include potential equations, calculations, or microstructural insights where relevant,Support statements with scientific reasoning,
+  Discuss potential variations or alternative interpretations
+  
+  
+  ### Instruction:
+  {}
+  
+  ### Input:
+  {}
+  
+  ### Response:
+  {}"""
+
+  from unsloth import FastLanguageModel
+  import torch
+  max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
+  dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
+  load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
+  
+  if True:
+      from unsloth import FastLanguageModel
+      model, tokenizer = FastLanguageModel.from_pretrained(
+          model_name = "Abdulrhman37/lora_model", # YOUR MODEL YOU USED FOR TRAINING
+          max_seq_length = max_seq_length,
+          dtype = dtype,
+          load_in_4bit = load_in_4bit,
+  
+      )
+      FastLanguageModel.for_inference(model) # Enable native 2x faster inference
+
+  ```
+3. **Use the fine tunned model**:
+  ```python
+  # function tp process question 
+  def answer(q: str):
+      """
+      Generates a detailed response to a metallurgy-related question using a pre-trained language model.
+  
+      Args:
+          q (str): The question or instruction to be answered.
+  
+      Returns:
+          str: The generated response from the model, specifically the content after "### Response:".
+      """
+  
+      # Initialize the language model for fast inference
+      FastLanguageModel.for_inference(model)  # Enables 2x faster native inference
+  
+      # Format the input question using the metallurgy prompt template
+      inputs = tokenizer(
+          [
+              metallurgy_prompt.format(
+                  q,  # Instruction: The main question
+                  "",  # Input: Empty for now as no specific input is provided
+                  ""   # Output: Placeholder for the generated response
+              )
+          ],
+          return_tensors="pt"  # Return input tensors
+      ).to("cuda")  # Transfer tensors to GPU for faster computation
+  
+      # Generate the model's output based on the formatted input
+      outputs = model.generate(**inputs, use_cache=True)  # Use cached values to speed up decoding
+  
+      # Decode the model's output into readable text
+      result = tokenizer.batch_decode(outputs)
+  
+      # Split the result into sections before and after "### Response:"
+      split_content = result[0].split("### Response:")
+      before_response = split_content[0].strip()  # Extract content before "Response"
+      after_response = split_content[1].strip().replace('<|end_of_text|>', '')  # Clean up response content
+  
+      # Prepare a detailed response dictionary for debugging or additional processing
+      detailed = {
+          'after_response': after_response,  # The main content of the generated response
+          'before_response': before_response,  # Metadata or introductory content before the response
+          'full_result': result  # The full raw output from the model
+      }
+  
+      # Return only the generated response content
+      return detailed['after_response']
+
+
+  # asking model a technical question 
+  q="To improve strength, toughness, and shock-resistance in Mg-Al-Mn system cast magnesium alloys (e.g. AM100A),what should I do ?"
+  
+  from pprint import pprint
+  pprint(answer(q))
+  ```
+
+  
+
 follow this [notebook](https://colab.research.google.com/drive/1pRNcAtybNF6w6mE1ZReFwfrIujZ5_t4S#scrollTo=wk4fCWOl0Ocd) for help to use the model
 
 ## 📧 Contact