初始化项目，由ModelHub XC社区提供模型
Model: codefuse-ai/SWE-CARE-RM Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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- zh
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base_model:
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- Qwen/Qwen3-8B
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library_name: transformers
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tags:
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- rm
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- cr
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---
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# SWE-CARE-RM
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This model is a custom reward model built on top of **Qwen3-8B** with:
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- a merged **LoRA** adapter
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- an additional **projector head**
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- a scalar reward output in **[0, 1]**
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The model is designed to score the quality of a review conditioned on:
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1. an issue / problem statement
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2. a code patch
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3. a candidate review
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A higher score means the model considers the review better under the given issue and patch.
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## Model Architecture
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The model consists of:
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- base model: **Qwen3-8B**
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- adaptation: **LoRA**
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- reward head: a custom **MLP projector**
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- final score: `sigmoid(projector(last_hidden_state[:, -1]))`
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This repository contains the **merged decoder weights** together with `projector.pth`.
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## Input Format
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The model expects three text fields:
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- `issue`
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- `patch`
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- `review`
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During inference, the input is formatted as:
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```latex
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<issue>{issue}</issue><patch>{patch}</patch><review>{review}<review>
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```
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The score is computed from the last token hidden state.
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## Quick Start
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```latex
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from pathlib import Path
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import json
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import torch
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import torch.nn as nn
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from transformers import AutoModelForCausalLM, AutoTokenizer
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MODEL_DIR = "codefuse-ai/SWE-CARE-RM"
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MAX_SEQ_LEN = 51200
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MIN_REVIEW_LEN = 4096
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TRUST_REMOTE_CODE = True
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with open(f"{MODEL_DIR}/data_sample.jsonl", "r") as fr:
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for line in fr:
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json_data = json.loads(line)
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break
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SAMPLE = {
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"issue": json_data['problem_statement'],
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"patch": json_data['patch_to_review'],
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"review": json_data['pos_review'][0]
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}
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class Projector(nn.Module):
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def __init__(self, arch, input_size, hidden_size, use_bf16):
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super().__init__()
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depth = int(arch[len("mlp"): arch.index("x_relu")])
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layers = [nn.Linear(input_size, hidden_size).bfloat16() if use_bf16 else
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nn.Linear(input_size, hidden_size)]
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for _ in range(1, depth):
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layers.append(nn.ReLU())
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layers.append(nn.Linear(hidden_size, 1).bfloat16() if use_bf16 else
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nn.Linear(hidden_size, 1))
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self.model = nn.Sequential(*layers)
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def forward(self, x):
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return self.model(x)
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def resolve_dtype(dtype_name):
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if dtype_name in {"bf16", "bfloat16"}:
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return torch.bfloat16
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if dtype_name in {"fp16", "float16"}:
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return torch.float16
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return torch.float32
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def infer_proj_arch(projector_state_dict):
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linear_weight_keys = [k for k in projector_state_dict if k.startswith("model.")
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and k.endswith(".weight")]
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return f"mlp{len(linear_weight_keys)}x_relu"
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def process_one(issue_ids, issue_masks, patch_ids, patch_masks, review_ids,
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review_masks, max_len, min_review_len):
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review_keep = min(min_review_len, len(review_ids))
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remain_for_patch = max(max_len - len(issue_ids) - review_keep, 0)
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patch_keep = min(len(patch_ids), remain_for_patch)
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ids_all = issue_ids + patch_ids[:patch_keep] + review_ids[-review_keep:]
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masks_all = issue_masks + patch_masks[:patch_keep] + review_masks[-review_keep:]
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if len(ids_all) < max_len:
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pad_len = max_len - len(ids_all)
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ids_all = [0] * pad_len + ids_all
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masks_all = [0] * pad_len + masks_all
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return ids_all[:max_len], masks_all[:max_len]
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reward_config = {}
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reward_config_path = Path(MODEL_DIR) / "reward_config.json"
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if reward_config_path.exists():
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reward_config = json.load(open(reward_config_path, "r", encoding="utf-8"))
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projector_path = Path(MODEL_DIR) / "projector.pth"
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projector_state_dict = torch.load(projector_path, map_location="cpu")
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proj_arch = reward_config.get("proj_arch") or infer_proj_arch(projector_state_dict)
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torch_dtype = resolve_dtype(reward_config.get("torch_dtype") or "bfloat16")
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attn_implementation = reward_config.get("attn_implementation")
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tokenizer = AutoTokenizer.from_pretrained(MODEL_DIR,
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trust_remote_code=TRUST_REMOTE_CODE, padding_side="left")
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model_kwargs = {"trust_remote_code": TRUST_REMOTE_CODE, "torch_dtype": torch_dtype}
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if attn_implementation:
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model_kwargs["attn_implementation"] = attn_implementation
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decoder = AutoModelForCausalLM.from_pretrained(MODEL_DIR, **model_kwargs)
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projector = Projector(proj_arch, decoder.config.hidden_size,
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decoder.config.hidden_size, torch_dtype == torch.bfloat16)
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projector.load_state_dict(projector_state_dict)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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decoder.to(device).eval()
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projector.to(device).eval()
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issue_inputs = tokenizer(f"<issue>{SAMPLE['issue']}</issue>", padding=False,
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truncation="longest_first")
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patch_inputs = tokenizer(f"<patch>{SAMPLE['patch']}</patch>", padding=False,
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truncation="longest_first")
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review_inputs = tokenizer(SAMPLE["review"], padding=False, truncation="longest_first")
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input_ids, attention_mask = process_one(
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issue_inputs["input_ids"],
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issue_inputs["attention_mask"],
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patch_inputs["input_ids"],
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patch_inputs["attention_mask"],
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review_inputs["input_ids"],
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review_inputs["attention_mask"],
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max_len=MAX_SEQ_LEN,
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min_review_len=MIN_REVIEW_LEN,
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)
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inputs = {
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"input_ids": torch.tensor([input_ids], dtype=torch.long, device=device),
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"attention_mask": torch.tensor([attention_mask], dtype=torch.long, device=device),
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}
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with torch.no_grad():
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hidden_state = decoder(**inputs, output_hidden_states=True).hidden_states[-1]
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reward = torch.sigmoid(projector(hidden_state).squeeze(-1)[:, -1]).item()
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print(reward)
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```
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## Output
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The model outputs a single scalar reward score in [0, 1].
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Typical interpretation:
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- higher score: better review quality
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- lower score: worse review quality
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This score is best used for:
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- ranking candidate reviews
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- pairwise comparison
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- reward modeling in downstream training or reranking
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## Intended Use
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This model is intended for:
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- code review quality scoring
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- reward modeling for review generation
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- reranking multiple candidate reviews for the same issue and patch
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## Limitations
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- The score is relative, not an absolute guarantee of correctness.
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- Long-input truncation may affect results.
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- The model should not be used as the only signal for production-critical review
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decisions.
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## Citation
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If you use this model, please cite SWE-CARE as appropriate.
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```
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@misc{guo2025codefusecrbenchcomprehensivenessawarebenchmarkendtoend,
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title={CodeFuse-CR-Bench: A Comprehensiveness-aware Benchmark for End-to-End Code Review Evaluation in Python Projects},
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author={Hanyang Guo and Xunjin Zheng and Zihan Liao and Hang Yu and Peng DI and Ziyin Zhang and Hong-Ning Dai},
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year={2025},
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eprint={2509.14856},
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archivePrefix={arXiv},
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primaryClass={cs.SE},
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url={https://arxiv.org/abs/2509.14856},
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}
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```
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"head_dim": 128,
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"hidden_act": "silu",
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"initializer_range": 0.02,
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"intermediate_size": 12288,
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"max_position_embeddings": 40960,
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"max_window_layers": 36,
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"model_type": "qwen3",
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"num_attention_heads": 32,
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"num_hidden_layers": 36,
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"num_key_value_heads": 8,
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"rope_theta": 1000000,
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"tie_word_embeddings": false,
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"transformers_version": "4.51.1",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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"top_p": 0.95,
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"transformers_version": "4.51.1"
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3
projector.pth
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|
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Normal file
11
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Normal file
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31
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Normal file
31
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Normal file
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BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
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Normal file
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241
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Normal file
241
tokenizer_config.json
Normal file
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|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n {%- if enable_thinking is defined and enable_thinking is false %}\n {{- '<think>\\n\\n</think>\\n\\n' }}\n {%- endif %}\n{%- endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
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