Sweep Next-Edit predicts your next code edit before you make it. It runs locally on your laptop in under 500ms (with speculative decoding) and outperforms models over 4x its size on next-edit benchmarks. More details here.

Usage

HF Transformers

See usage with 🤗 Transformers

import time
import difflib
from transformers import AutoTokenizer, Qwen2ForCausalLM
import torch

# Load model and tokenizer
model_id = "Xenova/sweep-next-edit-1.5B"

print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_id)

print("Loading model...")
model = Qwen2ForCausalLM.from_pretrained(model_id, device_map="auto")


def build_prompt(
    context_files: dict[str, str],
    recent_diffs: list[dict[str, str]],
    file_path: str,
    original_content: str,
    current_content: str,
) -> str:
    """
    Build a prompt following Sweep Next Edit's training format.

    Format:
        <|file_sep|>{file_path_1}
        {file_content_1}
        <|file_sep|>{file_path_2}
        {file_content_2}
        <|file_sep|>{changed_file_1}.diff
        original:
        {before_changes_of_diff}
        updated:
        {after_changes_of_diff}
        <|file_sep|>original/{file_path}
        {contents_prior_to_most_recent_change}
        <|file_sep|>current/{file_path}
        {current_state_of_contents}
        <|file_sep|>updated/{file_path}
        {updated_state_of_contents}

    Args:
        context_files: Dict mapping file paths to their contents (related files for context)
        recent_diffs: List of dicts with 'file_path', 'original', and 'updated' keys
        file_path: Path of the file being edited
        original_content: Contents prior to most recent change
        current_content: Current state of the file being edited

    Returns:
        Formatted prompt string
    """
    prompt_parts = []

    # Add context files
    for path, content in context_files.items():
        prompt_parts.append(f"<|file_sep|>{path}")
        prompt_parts.append(content)

    # Add recent diffs
    for diff in recent_diffs:
        prompt_parts.append(f"<|file_sep|>{diff['file_path']}.diff")
        prompt_parts.append("original:")
        prompt_parts.append(diff['original'])
        prompt_parts.append("updated:")
        prompt_parts.append(diff['updated'])

    # Add original and current states
    prompt_parts.append(f"<|file_sep|>original/{file_path}")
    prompt_parts.append(original_content)
    prompt_parts.append(f"<|file_sep|>current/{file_path}")
    prompt_parts.append(current_content)
    prompt_parts.append(f"<|file_sep|>updated/{file_path}")

    return "\n".join(prompt_parts)


def generate(prompt: str, max_new_tokens: int = 512) -> str:
    """Generate completion using the Sweep Next Edit model."""
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

    # Get the stop token ids
    stop_token_ids = [
        tokenizer.convert_tokens_to_ids("<|file_sep|>"),
        tokenizer.eos_token_id,
    ]

    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            do_sample=False,  # Use greedy decoding for deterministic output
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=stop_token_ids,
        )

    # Decode only the generated tokens (exclude the prompt)
    generated_ids = outputs[0][inputs["input_ids"].shape[1]:]
    generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True)

    return generated_text


if __name__ == "__main__":
    # Simple example: User is writing a greeting function
    # The model predicts what they'll write next based on the pattern

    file_path = "greet.py"

    # Context: Other files in the codebase
    context_files = {
        "utils.py": """def get_time_of_day():
    from datetime import datetime
    hour = datetime.now().hour
    if hour < 12:
        return "morning"
    elif hour < 18:
        return "afternoon"
    else:
        return "evening"
""",
    }

    # Recent changes: User just added a personalized greeting
    recent_diffs = [
        {
            "file_path": "greet.py",
            "original": """def greet():
    print("Hello!")""",
            "updated": """def greet(name):
    print(f"Hello, {name}!")""",
        }
    ]

    # Before the most recent change
    original_content = """def greet(name):
    print(f"Hello, {name}!")

greet("Alice")"""

    # Current state: User just imported get_time_of_day
    current_content = """from utils import get_time_of_day

def greet(name):
    print(f"Hello, {name}!")

greet("Alice")"""

    prompt = build_prompt(
        context_files=context_files,
        recent_diffs=recent_diffs,
        file_path=file_path,
        original_content=original_content,
        current_content=current_content,
    )

    print("\n" + "=" * 80)
    print("CURRENT CODE:")
    print("=" * 80)
    print(current_content)

    print("\nGenerating prediction...")
    start_time = time.time()
    predicted_edit = generate(prompt)
    end_time = time.time()

    print("\n" + "=" * 80)
    print("PREDICTED NEXT EDIT:")
    print("=" * 80)
    print(predicted_edit)

    print("\n" + "=" * 80)
    print(f"TIME TAKEN: {end_time - start_time:.2f} seconds")
    print("=" * 80)

    print("\n" + "=" * 80)
    print("DIFF (what changed):")
    print("=" * 80)

    diff = difflib.unified_diff(
        current_content.splitlines(keepends=True),
        predicted_edit.splitlines(keepends=True),
        fromfile=f"current/{file_path}",
        tofile=f"updated/{file_path}",
        lineterm=""
    )
    print("".join(diff))

Model Details

Parameters: 1.5B
Context Length: 8192 tokens
Base Model: Qwen2.5-Coder

License

Apache 2.0

Xenova/sweep-next-edit-1.5B

Sweep Next-Edit 1.5B (GGUF)

Model Description

Usage

HF Transformers

Model Details

Links

License

Xenova/
sweep-next-edit-1.5B