| # codetransformer-python-s | |
| ## Model Overview | |
| The `codetransformer-python-s` is a small-scale, decoder-only Transformer model fine-tuned specifically for generating and completing Python code. It is designed for speed and efficiency in environments where resource constraints are a concern, while maintaining a high degree of syntactic correctness and logical coherence for common programming tasks. | |
| ## Model Architecture | |
| * **Base Model:** Adapted from a scaled-down GPT-2 variant (similar to 350M parameter size). | |
| * **Architecture:** Causal Transformer (Decoder-only stack). | |
| * **Task:** Causal Language Modeling. It predicts the next token (line of code, function call, variable name, etc.) given the preceding context. | |
| * **Training Data:** Curated dataset of publicly available, high-quality Python repositories and popular algorithm implementations. | |
| ## Intended Use | |
| * **Code Completion:** Providing intelligent, multi-line suggestions within IDEs and code editors. | |
| * **Function Generation:** Generating boilerplate or utility functions from descriptive docstrings or comments. | |
| * **Educational Tool:** Assisting new programmers by demonstrating common language patterns and idiomatic Python usage. | |
| ## Limitations and Ethical Considerations | |
| * **Logic Errors:** The model is a text predictor, not a debugger or compiler. Generated code may contain subtle logical or runtime errors. | |
| * **Security Risks:** The model may reproduce insecure or vulnerable code patterns learned from its training data. **Generated code must be thoroughly audited before deployment.** | |
| * **Training Data Dependency:** It is heavily biased towards patterns present in its training corpus and may struggle with highly novel algorithms or external library APIs it has not encountered. | |
| * **Size Limitation:** Being a small model ('-s'), it has a limited context window (`n_ctx=1024`) and may fail to maintain consistency across very large files or complex projects. | |
| ## Example Code | |
| To generate Python code given a function signature: | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| # Load model and tokenizer | |
| model_name = "YourOrg/codetransformer-python-s" | |
| tokenizer = AutoTokenizer.from_pretrained(model_name) | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| # Define the prompt | |
| prompt = "def calculate_factorial(n):\n \"\"\"Calculates the factorial of a positive integer n.\"\"\"\n if n == 0:" | |
| input_ids = tokenizer.encode(prompt, return_tensors='pt') | |
| # Generate code | |
| output = model.generate( | |
| input_ids, | |
| max_length=100, | |
| num_return_sequences=1, | |
| do_sample=True, | |
| temperature=0.4, # Lower temperature for less creative, more deterministic code | |
| top_p=0.9, | |
| pad_token_id=tokenizer.eos_token_id | |
| ) | |
| generated_code = tokenizer.decode(output[0], skip_special_tokens=False) | |
| print("--- Generated Code Snippet ---") | |
| # Only the generated completion is valuable, but the full sequence is returned | |
| print(generated_code) |