初始化项目，由ModelHub XC社区提供模型

Model: launch/ThinkPRM-7B
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- reward-model
+- prm
+- generative reward model
+- process supervision
+- chain-of-thought
+- verification
+- math reasoning
+- code verification
+license: apache-2.0
+pipeline_tag: text-generation
+---
+
+# Model Card for ThinkPRM-7B
+
+ThinkPRM-7B is a generative Process Reward Model (PRM) based on the R1-Distill-Qwen-7B architecture. It is fine-tuned to perform step-by-step verification of reasoning processes (like mathematical solutions) by generating an explicit verification chain-of-thought (CoT) that involves labeling every step. It is designed to be highly data-efficient, requiring significantly less supervision data than traditional discriminative PRMs while achieving strong performance.
+
+Here's an example of the model output: 
+
+
+## Model Details
+
+### Model Description
+
+ThinkPRM-7B provides step-level verification scores by generating natural language critiques and correctness judgments for each step in a given solution prefix. It leverages the underlying reasoning capabilities of the base Large Reasoning Model (LRM) and enhances them through fine-tuning on a small (1K examples) dataset of synthetically generated verification CoTs. These synthetic CoTs were produced by prompting QwQ-32B-Preview and filtered against ground-truth step labels from the PRM800K dataset to ensure quality.
+
+The model uses a standard language modeling objective, making it interpretable and allowing it to scale process verification compute by generating longer or multiple verification CoTs. It demonstrated superior performance compared to LLM-as-a-judge and discriminative PRM baselines (based on the same R1-Distill-Qwen-7B model but trained on ~100x more labels) on benchmarks including ProcessBench, MATH-500, AIME '24, GPQA-Diamond, and LiveCodeBench.
+
+- **Finetuned from model [optional]:** [R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B)
+
+### Model Sources [optional]
+
+- **Repository:** [Github](https://github.com/mukhal/thinkprm)
+- **Paper:** [Process Reward Models that Think (arXiv:2504.16828)](https://arxiv.org/abs/2504.16828)
+
+
+### Direct Use
+
+ThinkPRM-7B is intended for verifying the correctness of step-by-step reasoning processes. Primary uses include:
+- **Scoring Solutions:** Assigning step-level or overall scores to candidate solutions for ranking in Best-of-N sampling or guiding tree search in reasoning tasks.
+- **Generating Verification Rationales/CoTs:** Producing detailed chain-of-thought verifications that explain *why* a particular step is correct or incorrect, aiding interpretability.
+- **Standalone Verification:** Evaluating the correctness of a given problem-solution pair.
+
+The model has been evaluated on mathematical reasoning (MATH, AIME), scientific QA (GPQA), and code generation (LiveCodeBench). See our paper for more details.
+
+## Limitations
+
+- **Overconfidence:** Generative PRMs like ThinkPRM can sometimes produce scores clustered near 0 or 1, potentially not reflecting true uncertainty
+- **Step Label Interference:** The autoregressive nature might cause an early incorrect step judgment to negatively bias the evaluation of subsequent steps.
+- **Sensitivity to Formatting/Prompting:** Performance might be sensitive to the exact format of the input solution and the prompt used for verification (though fine-tuning likely reduces this compared to LLM-as-a-judge).
+
+## How to Get Started with the Model
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from vllm import LLM, SamplingParams
+
+model_id = "launch/ThinkPRM-7B" # Replace with actual model ID on Hub
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+llm = LLM(model=model_id, max_model_len=16384)
+
+# Example problem and solution
+problem = "Solve for x: 2x + 3 = 7"
+prefix = "Step 1: Subtract 3 from both sides: 2x = 4
+Step 2: Divide by 2: x = 1"
+
+# Format the prompt
+prompt = f"""You are given a math problem and a proposed step-by-step solution:
+
+[Math Problem]
+
+{problem}
+
+[Solution]
+
+{prefix}
+
+Review and critique each step in the proposed solution to determine whether each step is correct. If the solution is incomplete, only verify the provided steps
+"""
+
+prompt = tokenizer.apply_chat_template([
+    {'role': "user", "content": prompt}
+], tokenize=False, add_generation_prompt=True) + "
+Let's verify step by step:"
+
+# Set sampling parameters
+sampling_params = SamplingParams(
+    temperature=0.0,
+    max_tokens=4096,
+    stop=None
+)
+
+# Generate the verification
+outputs = llm.generate(prompt, sampling_params)
+verification_cot = outputs[0].outputs[0].text
+
+print(verification_cot)
+"""
+Step 1: Subtract 3 from both sides: 2x = 4
+
+Critique: Starting with the equation 2x + 3 = 7, subtracting 3 from both sides is a correct operation to isolate the term with the variable. So, 2x + 3 - 3 = 7 - 3, which simplifies
+ to 2x = 4. This step seems correct.
+
+Step 2: Divide by 2: x = 1
+
+Critique: Now, to solve for x, we need to divide both sides of the equation by 2. So, 2x / 2 = 4 / 2, which simplifies to x = 2. Wait a minute, the solution says x = 1, but accordin
+g to this calculation, it should be x = 2. This seems incorrect.
+
+Therefore, the first step is correct, but the second step has an error.
+
+**Final Output:**
+
+Let's verify step by step:
+
+Step 1: Subtract 3 from both sides: 2x = 4
+
+Critique: This step is correct. Subtracting 3 from both sides of the equation 2x + 3 = 7 properly isolates the term with the variable, resulting in 2x = 4.
+
+Step 1 is \boxed{correct}
+
+Step 2: Divide by 2: x = 1
+
+Critique: This step is incorrect. Dividing both sides of the equation 2x = 4 by 2 should yield x = 2, not x = 1.
+
+Step 2 is \boxed{incorrect}
+</think>
+Is the solution correct? No
+"""