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README.md

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+# 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)