初始化项目，由ModelHub XC社区提供模型

Model: OpenRubrics/RubricARROW-8B-Rubric
Source: Original Platform

README.md

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+---
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3-8B
+---
+
+# OpenRubrics/RubricARROW-8B-Rubric
+
+This is an 8B rubric generation model, introduced in the paper [RUBRIC-ARROW: Alternating Pointwise Rubric Reward Modeling for LLM Post-training in Non-verifiable Domains](https://huggingface.co/papers/2605.29156). 
+
+It is finetuned from [Qwen3/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B).
+
+# Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "OpenRubrics/RubricARROW-8B-Rubric"
+tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto")
+```
+
+To evaluate the model, please use the following format to build up message.
+
+```python
+RUBRIC_PROMPT_TEMPLATE = (
+    "Your task is to extract a set of rubric-style instructions from a user's request.\n"
+    "These rubrics will be used as evaluation criteria to check if a response fully meets the request.\n"
+    "Every rubric item must be a universal principle. If any rubric still contains topic-specific references (e.g., names, places, myths, numbers, historical facts), it is automatically invalid.\n"
+    "\n"
+    "- **Two Distinct Categories:**\n"
+    "  - [Hard Rule]: Derived strictly from explicit requirements stated in the <request> (format, length, structure, forbidden/required elements, etc.).\n"
+    "  - [Principle]: Derived by abstracting any concrete cues into domain-agnostic quality criteria (e.g., clarity, correctness, sound reasoning, pedagogy).\n"
+    "\n"
+    "- **Comprehensiveness:**\n"
+    "  The rubric must cover all critical aspects implied by the request and examples, including explicit requirements and implicit quality standards.\n"
+    "\n"
+    "- **Conciseness & Uniqueness:**\n"
+    "  Each rubric must capture a distinct evaluation criterion. Overlapping or redundant criteria must be merged into a single rubric. Wording must be precise and free of repetition.\n"
+    "\n"
+    "- **Format Requirements:**\n"
+    "  - Use a numbered list.\n"
+    "  - Each item starts with \"The response\" phrased in third person.\n"
+    "  - Append [Hard Rule] or [Principle] at the end of each item.\n"
+    "  - Do not include reasoning, explanations, or examples in the final output—only the rubrics.\n"
+    "\n"
+    "Here is the request:\n"
+    "{prompt}\n"
+    "\n"
+    "Please generate the rubrics for the above request."
+)
+
+user_text = RUBRIC_PROMPT_TEMPLATE.format(
+    prompt=instruction,
+    )
+
+messages_list = [
+  {"role": "user", "content": user_text},
+]
+
+message = tok.apply_chat_template(
+  messages_list, 
+  tokenize=False, 
+  add_generation_prompt=True,
+  enable_thinking=False
+)
+
+# Remaining step: Use either HF or vLLM for evaluation.
+# ...
+# ...
+```
+
+
+# Citation
+
+If you find our work helpful, please consider citing our paper:
+
+```bibtex
+@misc{jiang2026rubric,
+      title={RUBRIC-ARROW: Alternating Pointwise Rubric Reward Modeling for LLM Post-training in Non-verifiable Domains}, 
+      author={Haoxiang Jiang and Zihan Dong and Tianci Liu and Wanying Wang and Ran Xu and Tony Yu and Linjun Zhang and Haoyu Wang},
+      year={2026},
+      eprint={2605.29156},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2605.29156}, 
+}
+```