Code Generation

Code Generation Models

from transformers import AutoModelForCausalLM, AutoTokenizer

class CodeGenerator:
    def __init__(self, model_name="codellama/CodeLlama-7b-hf"):
        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
        self.model = AutoModelForCausalLM.from_pretrained(model_name)

    def complete(self, prefix, max_tokens=100):
        inputs = self.tokenizer(prefix, return_tensors="pt")
        outputs = self.model.generate(
            **inputs,
            max_new_tokens=max_tokens,
            temperature=0.2,
            top_p=0.95
        )
        return self.tokenizer.decode(outputs[0], skip_special_tokens=True)

    def fill_middle(self, prefix, suffix, max_tokens=100):
        prompt = f"<PRE> {prefix} <SUF> {suffix} <MID>"
        inputs = self.tokenizer(prompt, return_tensors="pt")
        outputs = self.model.generate(
            **inputs,
            max_new_tokens=max_tokens
        )
        return self.tokenizer.decode(outputs[0])

Code-Specific Tokenization

# BPE tokenization for code
class CodeTokenizer:
    def __init__(self):
        self.special_tokens = {
            "INDENT": "<INDENT>",
            "DEDENT": "<DEDENT>",
            "NEWLINE": "<NEWLINE>",
            "SPACE": "<SPACE>"
        }

    def tokenize_with_structure(self, code):
        tokens = []
        indent_level = 0

        for line in code.split('\n'):
            current_indent = len(line) - len(line.lstrip())

            while indent_level * 4 < current_indent:
                tokens.append(self.special_tokens["INDENT"])
                indent_level += 1

            while indent_level * 4 > current_indent:
                tokens.append(self.special_tokens["DEDENT"])
                indent_level -= 1

            tokens.extend(self.tokenize_line(line.strip()))

        return tokens

Popular Code Models

Code Review Assistant

class CodeReviewAssistant:
    def __init__(self, code_model):
        self.model = code_model

    def review_code(self, code):
        prompt = f"""Review this code for potential issues:

```python
{{code}}
```

Identify:
1. Bugs or errors
2. Security vulnerabilities
3. Performance issues
4. Style improvements

Provide specific suggestions:"""

        return self.model.complete(prompt)

    def suggest_fix(self, code, error_message):
        prompt = f"""Fix this code error:

Code:
```python
{{code}}
```

Error: {{error_message}}

Fixed code:"""

        return self.model.complete(prompt)

Summary

Code generation models are transforming software development by automating coding tasks. Understanding their architecture and capabilities is essential for modern developers.

Next: We'll explore text-to-image generation.