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  library_name: transformers
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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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- 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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- - **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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- [More Information Needed]
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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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- <!-- This section is meant to convey both technical and sociotechnical 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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- #### 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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- ## 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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- #### Metrics
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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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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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- #### Hardware
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- ## Citation [optional]
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
 
 
 
 
 
 
 
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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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  ---
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+ **Repository for:**
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+ **ThinkEdit-deepseek-qwen-14b**
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+ (We also release ThinkEdit versions for ThinkEdit-deepseek-qwen-1.5b and ThinkEdit-deepseek-llama3-8b.)
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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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+ ---
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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 \~2% of "short reasoning" attention heads
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+ - Edits only \~0.1% 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-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** | **93.50** ± **0.31** | **96.53** ± **0.54** | **96.50** ± **0.46** | **91.15** ± **0.59** | **91.78** ± **0.58** |
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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** | **88.97** ± **0.78** | **96.08** ± **0.86** | **94.12** ± **0.47** | **85.91** ± **0.48** | **87.60** ± **0.81** |
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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** | **83.34** ± **0.79** | **86.24** ± **1.12** | **93.89** ± **0.76** | 74.94 ± 0.85 | **82.74** ± **0.77** |
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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-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** | **96.62** / **96.03** / **96.12** | **96.11** / **96.22** / **96.27** | **100.00** / **99.77** / **98.85** | **95.76** / **97.65** / **98.07** | **89.60** / **92.60** / **94.70** |
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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** | **92.46** / **92.37** / **92.05** | **77.22** / **80.54** / **79.73** | 96.19 / **95.81** / **97.36** | **93.79** / **95.83** / **95.80** | **92.80** / **94.40** / **94.90** |
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+ ---
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+ ### 3. Reasoning Lengths (Top 5% / 10% / 20% Shortest Responses)
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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-14b | 76.6 / 86.5 / 99.1 | 65.8 / 72.2 / 80.6 | 93.7 / 114.3 / 188.6 | 628.8 / 858.4 / 1125.9 | 198.7 / 434.3 / 697.0 |
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+ | **ThinkEdit-deepseek-qwen-14b** | **95.4** / **106.3** / **120.2** | **79.1** / **87.1** / **98.7** | **125.1** / **150.2** / **243.4** | **698.5** / **906.6** / **1157.2** | **270.2** / **492.6** / **733.3** |
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+ | deepseek-llama3-8b | 73.0 / 83.1 / 96.6 | 371.0 / 438.1 / 518.2 | 80.3 / 97.2 / 130.3 | 617.9 / 854.9 / 1126.5 | 159.5 / 357.5 / 644.5 |
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+ | **ThinkEdit-deepseek-llama3-8b** | **93.2** / **106.9** / **127.4** | **396.5** / **464.2** / **543.2** | **137.4** / **173.3** / **277.1** | **791.2** / **954.8** / **1185.1** | **305.2** / **506.3** / **737.6** |
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+ | deepseek-qwen-1.5b | 78.8 / 89.4 / 103.0 | 61.6 / 68.5 / 77.6 | 88.8 / 110.3 / 219.7 | 804.6 / **1017.9** / **1314.0** | 249.7 / 506.5 / 760.7 |
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+ | **ThinkEdit-deepseek-qwen-1.5b** | **97.2** / **109.4** / **126.3** | **75.9** / **85.0** / **99.5** | **127.9** / **174.1** / **416.4** | **818.0** / 984.5 / 1214.3 | **435.0** / **612.9** / **800.6** |
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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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+ }
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+ ```