Add library_name, pipeline_tag, and project page link
#2
by
nielsr
HF staff
- opened
README.md
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license: apache-2.0
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---
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# Universal-PRM-7B
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## 1. Overview
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Universal-PRM is trained using Qwen2.5-Math-7B-Instruct as the base. The training process incorporates diverse policy distributions, ensemble prompting, and reverse verification to enhance generalization and robustness. It achieves state-of-the-art performance on ProcessBench and the internally developed UniversalBench.
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## 2. Experiments
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judge_list_infer.append(reward)
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print(judge_list_infer) # [0.73828125, 0.7265625, 0.73046875, 0.73828125, 0.734375]
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```
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---
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license: apache-2.0
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library_name: transformers
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pipeline_tag: text-generation
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# Universal-PRM-7B
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Project page: https://auroraprm.github.io/
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## 1. Overview
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Universal-PRM is trained using Qwen2.5-Math-7B-Instruct as the base. The training process incorporates diverse policy distributions, ensemble prompting, and reverse verification to enhance generalization and robustness. It achieves state-of-the-art performance on ProcessBench and the internally developed UniversalBench.
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## 2. Experiments
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judge_list_infer.append(reward)
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print(judge_list_infer) # [0.73828125, 0.7265625, 0.73046875, 0.73828125, 0.734375]
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```
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