Update README.md

README.md

@@ -116,7 +116,101 @@ model = PeftModel.from_pretrained(
 ```
 
 ---
+## A worked example: a prompt in, code out
 
+  The prompt
+
+  PROMPT = """You are an expert Python programmer.
+
+  Solve the following programming problem.
+
+  Problem:
+  Calculate factorials for a list of numbers in parallel using multiprocessing.
+
+  It should return:
+  dict[int, int]: A dictionary with numbers as keys and their factorial as values.
+
+  It should raise:
+  ValueError: If any element in the input list is not an integer or is negative.
+
+  It may use:
+  multiprocessing.Pool
+  math.factorial.
+
+  Write a function with this exact signature:
+
+      def task_func(numbers: list) -> dict:
+
+  It must satisfy:
+
+      >>> factorials = task_func([5, 6, 7, 8, 9])
+      >>> factorials[5] == 120 and factorials[9] == 362880
+      True
+
+  Required function signature:
+  def task_func(numbers: list) -> dict:
+
+  Requirements:
+  - Implement the requested function.
+  - Follow the function signature.
+  - Handle the specified edge cases.
+  - Use Python.
+  - Return only the implementation.
+  - Do not provide an explanation.
+  - Do not use eval().
+  - Do not use exec().
+  - Do not perform network operations.
+  - Do not read or write files.
+  """
+
+### Generating
+
+
+  import re
+  import torch
+  from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
+
+  MODEL = "ranjanrajib/DPO-trained-Qwen2.5-Coder-7B"
+
+  model = AutoModelForCausalLM.from_pretrained(MODEL, dtype=torch.bfloat16, device_map="auto")
+  tok = AutoTokenizer.from_pretrained(MODEL)
+
+  text = tok.apply_chat_template(
+      [{"role": "user", "content": PROMPT}], tokenize=False, add_generation_prompt=True)
+  inputs = tok(text, return_tensors="pt").to(model.device)
+
+  set_seed(159000)                      # the seed this particular sample was drawn with
+  out = model.generate(
+      **inputs,
+      do_sample=True,
+      temperature=0.2,
+      top_p=0.95,
+      max_new_tokens=512,
+      pad_token_id=tok.eos_token_id,
+  )
+
+  response = tok.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True)
+  code = re.search(r"```python\n(.*?)```", response, re.S).group(1)
+  print(code)
+
+### What the model returned
+
+  84 generated tokens, reproduced verbatim:
+
+  import multiprocessing
+  import math
+
+  def factorial_task(number):
+      if not isinstance(number, int) or number < 0:
+          raise ValueError(f"Invalid input: {number}")
+      return number, math.factorial(number)
+
+  def task_func(numbers: list) -> dict:
+      with multiprocessing.Pool() as pool:
+          results = pool.map(factorial_task, numbers)
+      return dict(results)
+
+---
 ## Why Use This Model?
 
 If your primary use case is **Python code generation**, this model is intended to be a stronger alternative to the original Qwen2.5-Coder-7B-Instruct model based on the evaluation performed in this project.