commit 27cbf34fce1dc157f3991e8f4531ca67aa9153a5 Author: ModelHub XC Date: Thu Jul 23 14:56:09 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: Cyanophyte/sweep-next-edit-v2-7B-mlx-fp16 Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.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 +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs 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100644 index 0000000..ffea480 --- /dev/null +++ b/README.md @@ -0,0 +1,40 @@ +--- +license: apache-2.0 +base_model: sweepai/sweep-next-edit-v2-7B +tags: +- code +- autocomplete +- next-edit-prediction +- dpo +- mlx +- mlx-my-repo +language: +- en +pipeline_tag: text-generation +--- + +# Cyanophyte/sweep-next-edit-v2-7B-mlx-fp16 + +The Model [Cyanophyte/sweep-next-edit-v2-7B-mlx-fp16](https://huggingface.co/Cyanophyte/sweep-next-edit-v2-7B-mlx-fp16) was converted to MLX format from [sweepai/sweep-next-edit-v2-7B](https://huggingface.co/sweepai/sweep-next-edit-v2-7B) using mlx-lm version **0.31.2**. + +## Use with mlx + +```bash +pip install mlx-lm +``` + +```python +from mlx_lm import load, generate + +model, tokenizer = load("Cyanophyte/sweep-next-edit-v2-7B-mlx-fp16") + +prompt="hello" + +if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None: + messages = [{"role": "user", "content": prompt}] + prompt = tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) + +response = generate(model, tokenizer, prompt=prompt, verbose=True) +``` diff --git a/config.json b/config.json new file mode 100644 index 0000000..d62e38d --- /dev/null +++ b/config.json @@ -0,0 +1,60 @@ +{ + "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 +} \ No newline at end of file diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..dd5321e --- /dev/null +++ b/generation_config.json @@ -0,0 +1,8 @@ +{ + "bos_token_id": 151643, + "eos_token_id": 151643, + "max_length": 32768, + "max_new_tokens": 2048, + "pad_token_id": 151665, + "transformers_version": "4.51.3" +} diff --git a/inference.py b/inference.py new file mode 100644 index 0000000..55aff03 --- /dev/null +++ b/inference.py @@ -0,0 +1,386 @@ +""" +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 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