inference.py

"""inference.py - Code generation model wrapper for smolagents"""
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer


class CodeModel:
    def __init__(self, model_id: str, device: str = None):
        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
        self.tokenizer = AutoTokenizer.from_pretrained(model_id, fix_mistral_regex=True)
        dtype = torch.bfloat16 if self.device == "cuda" else torch.float32
        self.model = AutoModelForCausalLM.from_pretrained(model_id).to(self.device, dtype=dtype)
        self.model.eval()

    def generate(self, prompt: str, max_new_tokens: int = 512, temperature: float = 0.7) -> str:
        inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)

        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_new_tokens=max_new_tokens,
                temperature=temperature,
                do_sample=True,
                top_p=0.9,
                repetition_penalty=1.2,
                pad_token_id=self.tokenizer.pad_token_id,
                eos_token_id=self.tokenizer.eos_token_id,
            )

        new_tokens = outputs[0, inputs["input_ids"].shape[1]:]
        return self.tokenizer.decode(new_tokens, skip_special_tokens=False)

    def chat(self, messages: list[dict], max_new_tokens: int = 256) -> str:
        """Generate response using chat template."""
        text = self.tokenizer.apply_chat_template(
            messages,
            add_generation_prompt=True,
            tokenize=False
        )
        inputs = self.tokenizer(text, return_tensors="pt").to(self.device)

        with torch.no_grad():
            outputs = self.model.generate(
                **inputs,
                max_new_tokens=max_new_tokens,
                do_sample=True,
                temperature=0.7,
                top_p=0.9,
                repetition_penalty=1.2,
            )

        new_tokens = outputs[0, inputs["input_ids"].shape[1]:]
        return self.tokenizer.decode(new_tokens, skip_special_tokens=False)


if __name__ == "__main__":
    import os
    # Use local checkpoint if available, otherwise HuggingFace
    model_id = "checkpoint" if os.path.exists("checkpoint") else "AutomatedScientist/pynb-73m-base"
    model = CodeModel(model_id)

    # Example: Generate code
    result = model.generate("Write a Python function to calculate factorial")
    print("Generated code:")
    print(result)

    # Example: Chat
    messages = [
        {"role": "system", "content": "You are a helpful coding assistant."},
        {"role": "user", "content": "Write a function to reverse a string"}
    ]
    response = model.chat(messages)
    print("\nChat response:")
    print(response)