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Model: sweepai/sweep-next-edit-v2-7B
Source: Original Platform

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
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+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
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+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-7B
+tags:
+  - code
+  - autocomplete
+  - next-edit-prediction
+  - dpo
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# sweep-next-edit-v2-7B
+
+A 7B parameter model that predicts the next edit a developer will make. Given the current file, recent diffs, and cursor position, the model predicts what code block the developer will change next and how.
+
+## Usage
+
+```bash
+pip install transformers torch accelerate
+python inference.py
+```
+
+See [`inference.py`](inference.py) for a complete working example.
+
+```python
+from inference import build_prompt, generate, FileChunk, DIFF_FORMAT
+
+prompt, code_block, block_start, relative_cursor = build_prompt(
+    file_path="example.py",
+    file_contents=edited_contents,
+    cursor_position=cursor_position,
+    recent_changes=recent_changes,
+    retrieval_chunks=[FileChunk("utils.py", "def helper(): ...")],
+    changes_above_cursor=False,
+)
+
+completion = generate(model, tokenizer, prompt, device="cuda")
+```
+
+### Prompt format
+
+The model uses `<|file_sep|>` delimiters and a `<|cursor|>` marker:
+
+```
+<|file_sep|>{file_path}
+{file_contents}
+{retrieval_chunks}
+{recent_changes_as_diffs}
+<|file_sep|>original/{file_path}:{start}:{end}
+{code_block_before_last_edit}
+<|file_sep|>current/{file_path}:{start}:{end}
+{code_block_with_cursor_marker}
+<|file_sep|>updated/{file_path}:{start}:{end}
+{prefill}
+```
+
+The model completes the `updated/` section with the predicted new code block.
+
+- **file_path section**: ~300 lines of file context around the cursor
+- **retrieval chunks**: Cross-file context from related files
+- **recent changes**: Diffs of recent edits in `original:/updated:` format
+- **original/**: Code block around cursor before the last edit
+- **current/**: Same block with `<|cursor|>` inserted at cursor position
+- **updated/**: Model output — the predicted edited code block
+
+### Prefill strategy
+
+The `updated/` section is seeded with a prefill to constrain generation:
+
+- **Default** (`changes_above_cursor=False`): Prefill everything up to the cursor line. The model only generates from the cursor line onward.
+- **After insertion** (`changes_above_cursor=True`): Prefill only the first line + trailing blank lines. Gives the model freedom to rewrite lines between the insertion point and cursor.
+
+### Recent changes format
+
+```
+<|file_sep|>{file_path}:{start_line}:{end_line}
+original:
+{old_code}
+updated:
+{new_code}
+```
+
+## Details
+
+Fine-tuned from [Qwen2.5-Coder-7B](https://huggingface.co/Qwen/Qwen2.5-Coder-7B) on developer editing traces using SFT, then GRPO, then DPO.
+
+<table>
+<tr><td>Base model</td><td>Qwen2.5-Coder-7B</td></tr>
+<tr><td>Fine-tuning</td><td>SFT → GRPO → DPO</td></tr>
+<tr><td>Parameters</td><td>7B</td></tr>
+<tr><td>Precision</td><td>bfloat16</td></tr>
+<tr><td>Context length</td><td>32,768 tokens</td></tr>
+<tr><td>Architecture</td><td>Qwen2 (28 layers, hidden dim 3584)</td></tr>
+<tr><td>Stop tokens</td><td><code>&lt;|endoftext|&gt;</code>, <code>&lt;|file_sep|&gt;</code></td></tr>
+<tr><td>Max output tokens</td><td>1024</td></tr>
+<tr><td>Decoding</td><td>Greedy (temperature=0)</td></tr>
+</table>

added_tokens.json

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+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|PAD_TOKEN|>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "pad_token_id": 151665,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.51.3",
+  "unsloth_fixed": true,
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "max_length": 32768,
+  "max_new_tokens": 2048,
+  "pad_token_id": 151665,
+  "transformers_version": "4.51.3"
+}

inference.py

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+"""
+Minimal reproducible inference script for sweep-next-edit-v2-7B.
+
+This model predicts the next edit a developer will make given:
+  - the current file contents
+  - recent changes (diffs)
+  - the cursor position
+  - (optional) retrieval chunks from other files
+
+Usage:
+    python inference.py
+
+Requires: transformers, torch, accelerate
+    pip install transformers torch accelerate
+"""
+
+import torch
+from dataclasses import dataclass
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MODEL_ID = "sweepai/sweep-next-edit-v2-7B"
+
+# --- Prompt template (from sweepai/autocomplete/next_edit_autocomplete.py) ---
+PROMPT_TEMPLATE = """<|file_sep|>{file_path}
+{initial_file}{retrieval_results}
+{recent_changes}
+<|file_sep|>original/{file_path}:{start_line}:{end_line}
+{prev_section}
+<|file_sep|>current/{file_path}:{start_line}:{end_line}
+{code_block}
+<|file_sep|>updated/{file_path}:{start_line}:{end_line}
+{prefill}"""
+
+DIFF_FORMAT = """<|file_sep|>{file_path}:{start_line}:{end_line}
+original:
+{old_code}
+updated:
+{new_code}"""
+
+STOP_TOKENS = ["<|endoftext|>", "<|file_sep|>"]
+MAX_NEW_TOKENS = 1024
+
+
+@dataclass
+class FileChunk:
+    """A chunk of code from another file, used for cross-file context (retrieval)."""
+    file_path: str
+    content: str
+
+    def to_string(self) -> str:
+        return f"<|file_sep|>{self.file_path}\n{self.content}\n"
+
+
+def compute_prefill(
+    code_block: str,
+    relative_cursor: int,
+    changes_above_cursor: bool = False,
+) -> str:
+    """
+    Compute the prefill string — the portion of the updated code block that we
+    feed to the model so it only has to generate starting from the edit point.
+
+    The model's job is to produce the full "updated" code block. But most of it
+    is unchanged — only a small region near the cursor is different. So we
+    "prefill" the output with the unchanged prefix, and the model just continues
+    from there.
+
+    Two strategies depending on what the user just did:
+
+    changes_above_cursor=True (last action was an insertion):
+        The user just inserted text above the cursor. The lines above the cursor
+        may have shifted, so we can't trust them as a prefill — the model might
+        need to edit them. We only prefill the very first line of the code block
+        (plus any blank lines after it), giving the model freedom to rewrite
+        everything from line 2 onward.
+
+        Example: code_block is 11 lines, cursor on line 10.
+        Prefill = line 1 + any trailing blank lines = "    if n <= 0:\n"
+        Model generates lines 2-11.
+
+    changes_above_cursor=False (last action was NOT an insertion):
+        The user did something else (navigation, deletion, etc). The lines above
+        the cursor are likely stable, so we prefill up to the cursor line. This
+        constrains the model to only edit at/below the cursor.
+
+        We prefill everything before the cursor's line (up to the last newline
+        before cursor position), so the model starts generating from the cursor
+        line itself.
+
+        Example: code_block is 11 lines, cursor on line 10 col 0.
+        Prefill = lines 1-9 (everything up to the last \\n before cursor).
+        Model generates lines 10-11.
+    """
+    if changes_above_cursor:
+        # --- Insertion mode: only prefill first line + trailing newlines ---
+        prefill = code_block[:relative_cursor]
+        prefilled_lines = prefill.splitlines(True)
+
+        NUM_LINES_ABOVE = 1
+        before_split = "".join(prefilled_lines[:NUM_LINES_ABOVE])
+        after_split = "".join(prefilled_lines[NUM_LINES_ABOVE:])
+
+        # Append consecutive newlines (blank lines) but stop at first real char.
+        # This preserves blank-line structure without constraining the model
+        # to keep the original code on those lines.
+        for char in after_split:
+            if char == "\n":
+                before_split += "\n"
+            else:
+                break
+
+        return before_split
+    else:
+        # --- Default mode: prefill up to the cursor line ---
+        prefix_before_cursor = code_block[:relative_cursor]
+        if "\n" not in prefix_before_cursor:
+            # Cursor is on the first line — no prefill possible
+            return ""
+        prefill_end = prefix_before_cursor.rfind("\n") + 1
+        return code_block[:prefill_end]
+
+
+def is_pure_insertion_above_cursor(
+    code_block: str, completion: str, relative_cursor: int
+) -> bool:
+    """
+    Reject completions that only insert new lines above the cursor without
+    actually editing the cursor line. These are low-value predictions —
+    the model is just guessing what new code to add rather than fixing
+    an existing reference.
+    """
+    current_line_index = len(code_block[:relative_cursor].splitlines(True))
+    code_block_lines = code_block.splitlines(True)
+    cursor_line = code_block_lines[current_line_index - 1]
+
+    if code_block.strip() == completion.strip():
+        return False
+    if not cursor_line.strip():
+        return False
+
+    prefix_lines = code_block_lines[:current_line_index - 1]
+    prefix = "".join(prefix_lines)
+    suffix_lines = code_block_lines[current_line_index:]
+    suffix = "".join(suffix_lines)
+
+    # If completion = prefix + NEW STUFF + cursor_line + suffix, it's a pure
+    # insertion above cursor (nothing at/below cursor changed).
+    if completion.startswith(prefix) and completion.endswith(cursor_line + suffix):
+        return True
+
+    return False
+
+
+def build_prompt(
+    file_path: str,
+    file_contents: str,
+    cursor_position: int,
+    recent_changes: str = "",
+    retrieval_chunks: list[FileChunk] | None = None,
+    file_chunks: list[FileChunk] | None = None,
+    changes_above_cursor: bool = False,
+    num_lines_before: int = 10,
+    num_lines_after: int = 10,
+) -> tuple[str, str, int, int]:
+    """
+    Build the model prompt from file contents and cursor position.
+
+    Args:
+        file_path: Path of the file being edited.
+        file_contents: Full contents of the file after the user's latest edit.
+        cursor_position: Character offset of the cursor in file_contents.
+        recent_changes: Formatted diff string of recent changes (use DIFF_FORMAT).
+        retrieval_chunks: Cross-file context chunks (e.g. related functions from
+            other files). Placed AFTER recent_changes in the prompt for optimal
+            KV cache reuse.
+        file_chunks: Additional file context chunks. Prepended to the prompt.
+        changes_above_cursor: Whether the user's last action was an insertion.
+            Controls the prefill strategy (see compute_prefill).
+        num_lines_before: Lines of code to include before cursor in the block.
+        num_lines_after: Lines of code to include after cursor in the block.
+
+    Returns:
+        (formatted_prompt, code_block, block_start_index, relative_cursor)
+    """
+    lines = file_contents.splitlines(True)
+
+    # Find cursor line
+    pos = 0
+    cursor_line = 0
+    for i, line in enumerate(lines):
+        if pos + len(line) > cursor_position:
+            cursor_line = i
+            break
+        pos += len(line)
+    else:
+        cursor_line = len(lines) - 1
+
+    # Extract code block around cursor
+    block_start = max(0, cursor_line - num_lines_before)
+    block_end = min(len(lines), cursor_line + num_lines_after + 1)
+    code_block = "".join(lines[block_start:block_end])
+    block_start_index = sum(len(l) for l in lines[:block_start])
+
+    # Relative cursor position within code block
+    relative_cursor = cursor_position - block_start_index
+
+    # Insert <|cursor|> marker into the "current" version
+    code_block_with_cursor = (
+        code_block[:relative_cursor]
+        + "<|cursor|>"
+        + code_block[relative_cursor:]
+    )
+
+    # prev_section = code_block without cursor (the "original" version)
+    prev_section = code_block
+
+    # Compute prefill based on whether last action was an insertion
+    prefill = compute_prefill(code_block, relative_cursor, changes_above_cursor)
+
+    # initial_file: broad context around cursor from the file (up to ~300 lines)
+    context_start = max(0, cursor_line - 150)
+    context_end = min(len(lines), cursor_line + 150)
+    initial_file = "".join(lines[context_start:context_end])
+
+    # Format retrieval results (cross-file context)
+    retrieval_results = ""
+    if retrieval_chunks:
+        retrieval_results = "".join(
+            f"\n{chunk.to_string()}" for chunk in retrieval_chunks
+        )
+
+    start_line = block_start + 1
+    end_line = block_end
+
+    formatted = PROMPT_TEMPLATE.format(
+        file_path=file_path,
+        initial_file=initial_file,
+        retrieval_results=retrieval_results,
+        recent_changes=recent_changes,
+        prev_section=prev_section,
+        code_block=code_block_with_cursor,
+        start_line=start_line,
+        end_line=end_line,
+        prefill=prefill,
+    )
+
+    # Prepend file chunks (other open files for context)
+    if file_chunks:
+        formatted = "".join(c.to_string() for c in file_chunks) + formatted
+
+    return formatted, code_block, block_start_index, relative_cursor
+
+
+def generate(model, tokenizer, prompt: str, device: str = "cuda") -> str:
+    """Run inference and return the completion (the predicted updated code block)."""
+    inputs = tokenizer(prompt, return_tensors="pt").to(device)
+
+    stop_token_ids = [
+        tokenizer.convert_tokens_to_ids(t)
+        for t in STOP_TOKENS
+        if t in tokenizer.get_vocab()
+    ]
+    eos_ids = list(set(stop_token_ids + [tokenizer.eos_token_id]))
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=MAX_NEW_TOKENS,
+            do_sample=False,  # greedy (temperature=0)
+            eos_token_id=eos_ids,
+            pad_token_id=tokenizer.eos_token_id,
+        )
+
+    new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
+    completion = tokenizer.decode(new_tokens, skip_special_tokens=False)
+
+    # Strip stop tokens from output
+    for stop in STOP_TOKENS:
+        if stop in completion:
+            completion = completion[: completion.index(stop)]
+
+    return completion
+
+
+def main():
+    # --- Example: predict the next edit ---
+    file_path = "example.py"
+    file_contents = """\
+def fibonacci(n):
+    if n <= 0:
+        return 0
+    elif n == 1:
+        return 1
+    else:
+        return fibonacci(n - 1) + fibonacci(n - 2)
+
+
+def main():
+    for i in range(10):
+        print(fibonacci(i))
+"""
+
+    # Simulate: user just renamed fibonacci -> fib on line 7,
+    # cursor is now on line 12 (the call site that still says fibonacci).
+    edited_contents = file_contents.replace(
+        "return fibonacci(n - 1) + fibonacci(n - 2)",
+        "return fib(n - 1) + fib(n - 2)",
+    ).replace(
+        "def fibonacci(n):",
+        "def fib(n):",
+    )
+
+    # Cursor is on the print line that still references "fibonacci"
+    cursor_line_text = "        print(fibonacci(i))"
+    cursor_position = edited_contents.index(cursor_line_text)
+
+    # Recent change as a diff
+    recent_changes = DIFF_FORMAT.format(
+        file_path=file_path,
+        start_line=1,
+        end_line=7,
+        old_code="def fibonacci(n):\n        return fibonacci(n - 1) + fibonacci(n - 2)",
+        new_code="def fib(n):\n        return fib(n - 1) + fib(n - 2)",
+    )
+
+    # Example retrieval chunk: a related function from another file
+    retrieval_chunks = [
+        FileChunk(
+            file_path="utils.py",
+            content="def fib_memo(n, memo={}):\n    if n in memo:\n        return memo[n]\n    memo[n] = fib_memo(n-1) + fib_memo(n-2)\n    return memo[n]",
+        )
+    ]
+
+    # The rename was NOT an insertion, so changes_above_cursor=False.
+    # This means the prefill will include everything up to the cursor line,
+    # constraining the model to only edit at/below the cursor.
+    prompt, code_block, block_start, relative_cursor = build_prompt(
+        file_path=file_path,
+        file_contents=edited_contents,
+        cursor_position=cursor_position,
+        recent_changes=recent_changes,
+        retrieval_chunks=retrieval_chunks,
+        changes_above_cursor=False,
+    )
+
+    print("=" * 60)
+    print("PROMPT")
+    print("=" * 60)
+    print(prompt)
+    print()
+
+    # --- Load model and run inference ---
+    device = "mps" if torch.backends.mps.is_available() else "cpu"
+    print(f"Loading model {MODEL_ID} on {device}...")
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        MODEL_ID,
+        dtype=torch.bfloat16,
+        device_map=device,
+        trust_remote_code=True,
+    )
+
+    print("Running inference...")
+    completion = generate(model, tokenizer, prompt, device=device)
+
+    # Check for pure insertion above cursor (low-value prediction)
+    if is_pure_insertion_above_cursor(code_block, completion, relative_cursor):
+        print("Rejected: model only inserted above cursor without editing cursor line.")
+        return
+
+    print("=" * 60)
+    print("MODEL OUTPUT (predicted updated code block)")
+    print("=" * 60)
+    print(completion)
+    print()
+
+    # Show the diff
+    print("=" * 60)
+    print("DIFF")
+    print("=" * 60)
+    print(f"Original code block:\n{code_block}")
+    print(f"Updated code block:\n{completion}")
+
+
+if __name__ == "__main__":
+    main()

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