Create README.md

README.md

@@ -0,0 +1,81 @@
+---
+datasets:
+- iamtarun/python_code_instructions_18k_alpaca
+language:
+- en
+metrics:
+- code_eval
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- code
+widget:
+- text: 'def isprime(num):'
+  example_title: Code Example 1
+- text: 'def factorial(num):'
+  example_title: Code Example 2
+- text: 'def square(num):'
+  example_title: Code Example 3
+---
+
+# Competitive Programming LLM for Python Language
+
+This model is a finetuned version of [codegen350M-mono](https://huggingface.co/Salesforce/codegen-350M-mono) on python code [dataset](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca) that uses alpaca style prompts while training.
+
+## Prompt function
+
+```python
+'''
+This function generates prompts using the problem description and input.
+@param1 instruction: str - text problem description
+@param2 inputs: str - input to the program
+'''
+def generate_prompt(instruction, inputs=""):
+    text = ("Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n"
+            "### Instruction:\n"
+            f"{instruction}\n\n"
+            "### Input:\n"
+            f"{inputs}\n\n"
+            "### Output:\n")
+    return text
+```
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("iamtarun/codegen-350M-mono-4bit-qlora", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("iamtarun/codegen-350M-mono-4bit-qlora")
+
+# loading model for inference
+model.eval()
+
+# inference function
+'''
+This function takes text prompt as input which is generated from the generate_prompt function and returns the generated response
+
+@param1 prompt: str - text prompt generated using generate_prompt function.
+'''
+def pipe(prompt):
+    device = "cuda"
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+    with torch.no_grad():
+        output = model.generate(**inputs, 
+                                max_length=512,
+                                do_sample=True,
+                                temperature=0.5,
+                                top_p=0.95,
+                                repetition_penalty=1.15)
+    return tokenizer.decode(output[0].tolist(), 
+                            skip_special_tokens=True, 
+                            clean_up_tokenization_space=False)
+
+# generating code for a problem description
+instruction = "Write a function to calculate square of a number in python"
+inputs = "number = 5"
+prompt = generate_prompt(instruction, inputs)
+print(pipe(prompt))
+print("\n", "="*100)
+```