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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- ### Model Sources [optional]
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- - **Repository:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- ### Downstream Use [optional]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- ## Bias, Risks, and Limitations
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- ### Recommendations
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- ## Environmental Impact
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
 
 
 
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- ## Glossary [optional]
 
 
 
 
 
 
 
 
 
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- ## More Information [optional]
 
 
 
 
 
 
 
 
 
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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  library_name: transformers
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  tags: []
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+ pipeline_tag: text-generation
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+ license: mit
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+ base_model:
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+ - deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
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  ---
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+ **Repository for:**
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+ **ThinkEdit-deepseek-qwen-32b**
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+ (We also release ThinkEdit versions for ThinkEdit-deepseek-qwen-1.5b, ThinkEdit-deepseek-llama3-8b, and ThinkEdit-deepseek-qwen-14b.)
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+ **Authors**: Chung-En Sun, Ge Yan, Tsui-Wei Weng
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+ **Paper**: [ThinkEdit: Interpretable Weight Editing to Mitigate Overly Short Thinking in Reasoning Models](https://arxiv.org/abs/2503.22048)
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+ Github: https://github.com/Trustworthy-ML-Lab/ThinkEdit
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+ ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## Introduction
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+ Reasoning-augmented models sometimes fail by generating **overly short**, abstract chain-of-thought (CoT) reasoning, hurting their accuracy.
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+ **ThinkEdit** is a lightweight weight-editing method that:
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+ - Identifies ~4% of "short reasoning" attention heads
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+ - Edits only ~0.2% of total parameters
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+ - Removes the "short reasoning" direction from their output
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+ - Boosts performance, especially on cases with short reasoning traces
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+ ---
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+ ## Full Performance Results
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+ ### 1. Overall Accuracy
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+ | Model | GSM8K | MMLU Elementary Math | MATH-Level1 | MATH-Level5 | MATH-500 |
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+ |---------------------------------|---------------------|----------------------|---------------------|---------------------|---------------------|
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+ | deepseek-qwen-32b | 92.97 ± 0.39 | 95.93 ± 0.83 | **96.41 ± 0.45** | 91.27 ± 0.53 | **91.62 ± 0.58** |
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+ | **ThinkEdit-deepseek-qwen-32b** | **95.25 ± 0.25** | **98.02 ± 0.31** | 96.02 ± 0.42 | **91.31 ± 0.50** | 91.60 ± 0.65 |
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+ | deepseek-qwen-14b | 90.80 ± 0.36 | 95.08 ± 0.65 | 96.32 ± 0.35 | 90.25 ± 0.72 | 91.48 ± 0.55 |
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+ | **ThinkEdit-deepseek-qwen-14b** | **93.78 ± 0.50** | **96.56 ± 0.84** | **96.38 ± 0.52** | **91.03 ± 0.44** | **91.92 ± 0.63** |
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+ | deepseek-llama3-8b | 82.26 ± 0.91 | 96.01 ± 0.62 | 93.46 ± 0.84 | 85.49 ± 0.83 | 87.26 ± 1.16 |
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+ | **ThinkEdit-deepseek-llama3-8b**| **89.44 ± 0.55** | **96.19 ± 0.73** | **94.44 ± 0.31** | **86.49 ± 0.54** | **88.06 ± 1.09** |
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+ | deepseek-qwen-1.5b | 79.15 ± 1.08 | 68.52 ± 1.56 | 93.00 ± 0.33 | **75.48 ± 0.90** | 82.22 ± 1.29 |
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+ | **ThinkEdit-deepseek-qwen-1.5b**| **84.56 ± 0.79** | **90.66 ± 0.97** | **93.66 ± 0.62** | 75.05 ± 0.82 | **82.24 ± 0.89** |
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+ ---
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+ ### 2. Accuracy on Short Reasoning Cases (Top 5% / 10% / 20%)
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+ | Model | GSM8K | MMLU Elementary Math | MATH-Level1 | MATH-Level5 | MATH-500 |
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+ |---------------------------------|---------------------------------|----------------------------------|----------------------------------|----------------------------------|----------------------------------|
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+ | deepseek-qwen-32b | 98.31 / 97.18 / 96.20 | 97.78 / 97.03 / 95.87 | 100.00 / 100.00 / **98.97** | 93.03 / 96.36 / 97.35 | 86.40 / 92.00 / 94.00 |
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+ | **ThinkEdit-deepseek-qwen-32b** | **98.92** / **97.71** / **97.83** | **97.78** / **97.57** / **97.20** | **100.00** / **100.00** / 98.74 | **98.03** / **98.64** / **97.99** | **92.00** / **94.40** / **95.80** |
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+ | deepseek-qwen-14b | **96.31** / 95.65 / 92.93 | 93.89 / **96.22** / 95.60 | 99.52 / **99.30** / 97.70 | 89.39 / 94.32 / 96.25 | 86.40 / 91.40 / 93.50 |
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+ | **ThinkEdit-deepseek-qwen-14b** | **96.31** / **96.18** / **96.77** | **97.78** / 95.14 / **96.53** | **99.53** / 98.62 / **98.67** | **96.67** / **97.88** / **98.11** | **91.20** / **93.20** / **95.00** |
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+ | deepseek-llama3-8b | 88.92 / 87.18 / 85.82 | 97.22 / 96.49 / 96.80 | 97.14 / 94.88 / 94.83 | 78.64 / 88.79 / 93.41 | 82.00 / 81.40 / 88.30 |
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+ | **ThinkEdit-deepseek-llama3-8b**| **97.08** / **95.27** / **93.95** | **97.78** / **98.65** / **97.87** | **100.00** / **99.30** / **98.62** | **95.61** / **96.89** / **97.12** | **92.80** / **93.60** / **94.40** |
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+ | deepseek-qwen-1.5b | 88.46 / 87.48 / 85.02 | 62.78 / 62.16 / 60.53 | **97.62** / 95.12 / 93.91 | 91.52 / 95.00 / 95.72 | 82.40 / 89.80 / 93.40 |
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+ | **ThinkEdit-deepseek-qwen-1.5b**| **92.62** / **92.90** / **92.32** | **87.78** / **88.11** / **88.67** | 95.71 / **95.58** / **96.44** | **95.15** / **96.59** / **97.27** | **90.80** / **92.00** / **94.20** |
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+ ---
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+ ## Usage
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+ The usage of ThinkEdit models is exactly the same as the original deepseek-distilled models.
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+ ## Citation
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+ ```bibtex
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+ @misc{sun2025thinkedit,
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+ title={ThinkEdit: Interpretable Weight Editing to Mitigate Overly Short Thinking in Reasoning Models},
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+ author={Chung-En Sun and Ge Yan and Tsui-Wei Weng},
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+ year={2025},
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+ eprint={2503.22048},
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+ archivePrefix={arXiv},
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+ primaryClass={cs.CL},
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+ url={https://arxiv.org/abs/2503.22048},
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+ }