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Model: sweepai/sweep-next-edit-v2-7B Source: Original Platform
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.gitattributes
vendored
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99
README.md
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen2.5-Coder-7B
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tags:
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- code
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- autocomplete
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- next-edit-prediction
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- dpo
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language:
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- en
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pipeline_tag: text-generation
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---
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# sweep-next-edit-v2-7B
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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.
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## Usage
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```bash
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pip install transformers torch accelerate
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python inference.py
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```
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See [`inference.py`](inference.py) for a complete working example.
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```python
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from inference import build_prompt, generate, FileChunk, DIFF_FORMAT
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prompt, code_block, block_start, relative_cursor = build_prompt(
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file_path="example.py",
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file_contents=edited_contents,
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cursor_position=cursor_position,
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recent_changes=recent_changes,
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retrieval_chunks=[FileChunk("utils.py", "def helper(): ...")],
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changes_above_cursor=False,
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)
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completion = generate(model, tokenizer, prompt, device="cuda")
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```
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### Prompt format
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The model uses `<|file_sep|>` delimiters and a `<|cursor|>` marker:
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```
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<|file_sep|>{file_path}
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{file_contents}
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{retrieval_chunks}
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{recent_changes_as_diffs}
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<|file_sep|>original/{file_path}:{start}:{end}
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{code_block_before_last_edit}
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<|file_sep|>current/{file_path}:{start}:{end}
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{code_block_with_cursor_marker}
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<|file_sep|>updated/{file_path}:{start}:{end}
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{prefill}
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```
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The model completes the `updated/` section with the predicted new code block.
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- **file_path section**: ~300 lines of file context around the cursor
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- **retrieval chunks**: Cross-file context from related files
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- **recent changes**: Diffs of recent edits in `original:/updated:` format
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- **original/**: Code block around cursor before the last edit
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- **current/**: Same block with `<|cursor|>` inserted at cursor position
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- **updated/**: Model output — the predicted edited code block
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### Prefill strategy
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The `updated/` section is seeded with a prefill to constrain generation:
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- **Default** (`changes_above_cursor=False`): Prefill everything up to the cursor line. The model only generates from the cursor line onward.
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- **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.
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### Recent changes format
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```
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<|file_sep|>{file_path}:{start_line}:{end_line}
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original:
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{old_code}
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updated:
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{new_code}
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```
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## Details
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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.
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<table>
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<tr><td>Base model</td><td>Qwen2.5-Coder-7B</td></tr>
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<tr><td>Fine-tuning</td><td>SFT → GRPO → DPO</td></tr>
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<tr><td>Parameters</td><td>7B</td></tr>
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<tr><td>Precision</td><td>bfloat16</td></tr>
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<tr><td>Context length</td><td>32,768 tokens</td></tr>
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<tr><td>Architecture</td><td>Qwen2 (28 layers, hidden dim 3584)</td></tr>
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<tr><td>Stop tokens</td><td><code><|endoftext|></code>, <code><|file_sep|></code></td></tr>
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<tr><td>Max output tokens</td><td>1024</td></tr>
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<tr><td>Decoding</td><td>Greedy (temperature=0)</td></tr>
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</table>
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25
added_tokens.json
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added_tokens.json
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{
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"</tool_call>": 151658,
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"<tool_call>": 151657,
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"<|PAD_TOKEN|>": 151665,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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config.json
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151643,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"pad_token_id": 151665,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.51.3",
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"unsloth_fixed": true,
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 152064
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}
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8
generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"eos_token_id": 151643,
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"max_length": 32768,
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"max_new_tokens": 2048,
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"pad_token_id": 151665,
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"transformers_version": "4.51.3"
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}
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386
inference.py
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inference.py
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"""
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Minimal reproducible inference script for sweep-next-edit-v2-7B.
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This model predicts the next edit a developer will make given:
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- the current file contents
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- recent changes (diffs)
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- the cursor position
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- (optional) retrieval chunks from other files
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Usage:
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python inference.py
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Requires: transformers, torch, accelerate
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pip install transformers torch accelerate
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"""
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import torch
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from dataclasses import dataclass
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from transformers import AutoModelForCausalLM, AutoTokenizer
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MODEL_ID = "sweepai/sweep-next-edit-v2-7B"
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# --- Prompt template (from sweepai/autocomplete/next_edit_autocomplete.py) ---
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PROMPT_TEMPLATE = """<|file_sep|>{file_path}
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{initial_file}{retrieval_results}
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{recent_changes}
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<|file_sep|>original/{file_path}:{start_line}:{end_line}
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{prev_section}
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<|file_sep|>current/{file_path}:{start_line}:{end_line}
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{code_block}
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<|file_sep|>updated/{file_path}:{start_line}:{end_line}
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{prefill}"""
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DIFF_FORMAT = """<|file_sep|>{file_path}:{start_line}:{end_line}
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original:
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{old_code}
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updated:
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{new_code}"""
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STOP_TOKENS = ["<|endoftext|>", "<|file_sep|>"]
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MAX_NEW_TOKENS = 1024
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@dataclass
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class FileChunk:
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"""A chunk of code from another file, used for cross-file context (retrieval)."""
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file_path: str
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content: str
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def to_string(self) -> str:
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return f"<|file_sep|>{self.file_path}\n{self.content}\n"
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def compute_prefill(
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code_block: str,
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relative_cursor: int,
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changes_above_cursor: bool = False,
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) -> str:
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"""
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Compute the prefill string — the portion of the updated code block that we
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feed to the model so it only has to generate starting from the edit point.
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The model's job is to produce the full "updated" code block. But most of it
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is unchanged — only a small region near the cursor is different. So we
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"prefill" the output with the unchanged prefix, and the model just continues
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from there.
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Two strategies depending on what the user just did:
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changes_above_cursor=True (last action was an insertion):
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The user just inserted text above the cursor. The lines above the cursor
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may have shifted, so we can't trust them as a prefill — the model might
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need to edit them. We only prefill the very first line of the code block
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(plus any blank lines after it), giving the model freedom to rewrite
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everything from line 2 onward.
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Example: code_block is 11 lines, cursor on line 10.
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Prefill = line 1 + any trailing blank lines = " if n <= 0:\n"
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Model generates lines 2-11.
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changes_above_cursor=False (last action was NOT an insertion):
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The user did something else (navigation, deletion, etc). The lines above
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the cursor are likely stable, so we prefill up to the cursor line. This
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constrains the model to only edit at/below the cursor.
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We prefill everything before the cursor's line (up to the last newline
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before cursor position), so the model starts generating from the cursor
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line itself.
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Example: code_block is 11 lines, cursor on line 10 col 0.
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Prefill = lines 1-9 (everything up to the last \\n before cursor).
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Model generates lines 10-11.
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"""
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if changes_above_cursor:
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# --- Insertion mode: only prefill first line + trailing newlines ---
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prefill = code_block[:relative_cursor]
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prefilled_lines = prefill.splitlines(True)
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NUM_LINES_ABOVE = 1
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before_split = "".join(prefilled_lines[:NUM_LINES_ABOVE])
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after_split = "".join(prefilled_lines[NUM_LINES_ABOVE:])
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# Append consecutive newlines (blank lines) but stop at first real char.
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# This preserves blank-line structure without constraining the model
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# to keep the original code on those lines.
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for char in after_split:
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if char == "\n":
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before_split += "\n"
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else:
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break
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return before_split
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else:
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# --- Default mode: prefill up to the cursor line ---
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prefix_before_cursor = code_block[:relative_cursor]
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if "\n" not in prefix_before_cursor:
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# Cursor is on the first line — no prefill possible
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return ""
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prefill_end = prefix_before_cursor.rfind("\n") + 1
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return code_block[:prefill_end]
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def is_pure_insertion_above_cursor(
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code_block: str, completion: str, relative_cursor: int
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) -> bool:
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"""
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Reject completions that only insert new lines above the cursor without
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actually editing the cursor line. These are low-value predictions —
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the model is just guessing what new code to add rather than fixing
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an existing reference.
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"""
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current_line_index = len(code_block[:relative_cursor].splitlines(True))
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code_block_lines = code_block.splitlines(True)
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cursor_line = code_block_lines[current_line_index - 1]
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||||
if code_block.strip() == completion.strip():
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return False
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||||
if not cursor_line.strip():
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return False
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||||
prefix_lines = code_block_lines[:current_line_index - 1]
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prefix = "".join(prefix_lines)
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suffix_lines = code_block_lines[current_line_index:]
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suffix = "".join(suffix_lines)
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# If completion = prefix + NEW STUFF + cursor_line + suffix, it's a pure
|
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# insertion above cursor (nothing at/below cursor changed).
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if completion.startswith(prefix) and completion.endswith(cursor_line + suffix):
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return True
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||||
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||||
return False
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||||
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def build_prompt(
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file_path: str,
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file_contents: str,
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cursor_position: int,
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recent_changes: str = "",
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retrieval_chunks: list[FileChunk] | None = None,
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file_chunks: list[FileChunk] | None = None,
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changes_above_cursor: bool = False,
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num_lines_before: int = 10,
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num_lines_after: int = 10,
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) -> tuple[str, str, int, int]:
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||||
"""
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Build the model prompt from file contents and cursor position.
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Args:
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file_path: Path of the file being edited.
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file_contents: Full contents of the file after the user's latest edit.
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cursor_position: Character offset of the cursor in file_contents.
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recent_changes: Formatted diff string of recent changes (use DIFF_FORMAT).
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retrieval_chunks: Cross-file context chunks (e.g. related functions from
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other files). Placed AFTER recent_changes in the prompt for optimal
|
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KV cache reuse.
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file_chunks: Additional file context chunks. Prepended to the prompt.
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changes_above_cursor: Whether the user's last action was an insertion.
|
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Controls the prefill strategy (see compute_prefill).
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num_lines_before: Lines of code to include before cursor in the block.
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||||
num_lines_after: Lines of code to include after cursor in the block.
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||||
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Returns:
|
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(formatted_prompt, code_block, block_start_index, relative_cursor)
|
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"""
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lines = file_contents.splitlines(True)
|
||||
|
||||
# Find cursor line
|
||||
pos = 0
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||||
cursor_line = 0
|
||||
for i, line in enumerate(lines):
|
||||
if pos + len(line) > cursor_position:
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||||
cursor_line = i
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||||
break
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||||
pos += len(line)
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||||
else:
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cursor_line = len(lines) - 1
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||||
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# Extract code block around cursor
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||||
block_start = max(0, cursor_line - num_lines_before)
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block_end = min(len(lines), cursor_line + num_lines_after + 1)
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||||
code_block = "".join(lines[block_start:block_end])
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block_start_index = sum(len(l) for l in lines[:block_start])
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||||
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# 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()
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00004.safetensors
Normal file
3
model-00001-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
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size 4877660776
|
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model-00002-of-00004.safetensors
Normal file
3
model-00002-of-00004.safetensors
Normal file
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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Normal file
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3
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Normal file
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version https://git-lfs.github.com/spec/v1
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size 1089994880
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346
model.safetensors.index.json
Normal file
346
model.safetensors.index.json
Normal file
@@ -0,0 +1,346 @@
|
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{
|
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|
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|
||||
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|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
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|
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||||
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|
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|
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|
||||
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|
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|
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||||
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|
||||
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|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a11add1ccce473bcd467355ba4e231ba727eee54501528658b0d6fa18112957e
|
||||
size 11422253
|
||||
219
tokenizer_config.json
Normal file
219
tokenizer_config.json
Normal file
@@ -0,0 +1,219 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
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|
||||
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|
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|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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||||
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|
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||||
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|
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
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|
||||
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||||
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|
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||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user