Update README.md

README.md

@@ -46,7 +46,58 @@ tags:
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+```python
+
+from huggingface_hub import login
+from transformers import BitsAndBytesConfig, AutoModelForCausalLM, AutoTokenizer
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, pipeline
+
+
+config = PeftConfig.from_pretrained("smartinez1/Llama-3.1-8B-FINLLM")
+base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B")
+model = PeftModel.from_pretrained(base_model, "smartinez1/Llama-3.1-8B-FINLLM")
+# Load the tokenizer associated with the base model
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B")
+
+# Set up the text generation pipeline with the PEFT model, specifying the device
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device)
+
+# List of user inputs
+user_inputs = [
+    "Provide a link for Credit Card Accountability Responsibility and Disclosure Act law.",
+    "Define the following term: National Automated Clearing House Association.",
+    "Expand the following acronym into its full form: CIA."
+]
+
+# Define the prompt template
+prompt_template = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{0}
+
+### Answer:
+{1}
+"""
+
+# Loop over each user input and generate a response
+for user_input in user_inputs:
+    # Format the user input into the prompt
+    prompt = prompt_template.format(user_input, "")
+
+    # Generate a response from the model
+    response = generator(prompt, max_length=200, num_return_sequences=1, do_sample=True)
+
+    # Extract and clean up the AI's response
+    response_str = response[0]['generated_text'].split('### Answer:')[1].strip()
+    cut_ind = response_str.find("#")  # Remove extra information after the response
+    response_str = response_str[:cut_ind].strip() if cut_ind != -1 else response_str
+
+    # Display the AI's response
+    print(f"User: {user_input}")
+    print(f"AI: {response_str}")
+    print("-" * 50)  # Separator for clarity
 
 ### Downstream Use [optional]