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  library_name: transformers
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- tags: []
 
 
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  ---
 
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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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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
 
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- <!-- Provide the basic links for the model. -->
 
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [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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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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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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- [More Information Needed]
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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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- [More Information Needed]
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- ### Recommendations
 
 
 
 
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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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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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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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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- [More Information Needed]
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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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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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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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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [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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- **APA:**
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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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- [More Information Needed]
 
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  ---
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  library_name: transformers
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+ license: apache-2.0
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+ language:
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+ - ko
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  ---
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+ # GPT-4o Level with Ko-R1-1.5B
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+ ## Model Overview
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+ Ko-R1-Preview is a Korean implementation of [R1](https://huggingface.co/deepseek-ai/DeepSeek-R1).
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+ Similar to R1, it adapts a two-step problem-solving process (<think> and <solution>). Drawing inspiration from [Ko et al., 2025](https://www.arxiv.org/abs/2501.02448) the thinking is done in English for performance, and the solution is generated in Korean for readability.
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+ ## Performance
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+ The model’s performance is evaluated on subsets of the HRM8K benchmark ([Ko et al., 2025](https://www.arxiv.org/abs/2501.02448)).
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+ The evaluation files are also available here.
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+ ## Inference Settings
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+ Parameters used during evaluation:
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+ ```
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+ params = SamplingParams(temperature=0.0, min_tokens=32, max_tokens=8192, repetition_penalty=1.1)
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+ ```
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+ ## Sample Output
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+ Here is a sample output.
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+ ```
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+ Q: $2^8=4^x$일 때, $x$의 값은 얼마입니까?
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+ Response by ko-r1-preview-1.5B:
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+ <think>
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+ Okay, so I need to solve the equation 2^8 = 4^x and find the value of x. Let me think about how to approach this step by step.
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+ First, I remember that when dealing with exponential equations, it's often helpful to express both sides of the equation with the same base if possible.
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+ That way, I can set the exponents equal to each other because if a^b = c^d, then b must equal d if a and c are the same.
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+ So, let's see if I can rewrite 4 as a power of 2. Since 4 is 2 squared, right? Because 2 times 2 is 4. So, 4 is 2^2.
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+ So substituting that into the original equation, we have:\n\n2^8 = (2^2)^x
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+ Now, using the exponentiation rule (a^m)^n = a^(m*n), which means multiplying the exponents. Applying that here, the right side becomes 2^(2*x).
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+ So now the equation is:\n\n2^8 = 2^(2x)
 
 
 
 
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+ Since the bases are the same (both are powers of 2), their exponents must be equal for the equality to hold.
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+ Therefore, I can set the exponents equal to each other:\n\n8 = 2x\n\nTo solve for x, I just divide both sides by 2:
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+ x = 8 / 2
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+ x = 4
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+ Wait, let me check that again. If x is 4, then 4^4 would be 256, and 2^8 is also 256. Yeah, that works out.
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+ But maybe there's another way to verify?
 
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+ Alternatively, I could take logarithms on both sides.
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+ Let's try that method to confirm. Taking the natural log or common log of both sides.
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+ Let's use natural logs for this example.\n\nTaking ln of both sides:
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+ ln(2^8) = ln(4^x)
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+ Using the logarithm property that ln(a^b) = b*ln(a):
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+ 8*ln(2) = x*ln(4)\n\nThen solving for x:
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+ x = (8*ln(2)) / ln(4)
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+ But since 4 is 2^2, ln(4) is ln(2^2) = 2*ln(2). Substituting back in:
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+ x = (8*ln(2)) / (2*ln(2)) = 8/2 = 4
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+ Same result. So that confirms it. Both methods give x = 4.
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+ [skipped...]
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+ </think>
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+ <solution>
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+ \\(2^8 = 4^x\\)을 풀기 위해, 우리는 양쪽을 같은 밑으로 표현합니다. \\(4\\)를 \\(2^2\\)로 다시 씁니다:
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+ \\[
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+ 2^8 = (2^2)^x
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+ \\]
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+ 지수 법칙 \\((a^m)^n = a^{mn}\\)을 사용하여 오른쪽을 단순화하면:
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+ \\[\n2^8 = 2^{2x}\n\\]
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+ 밑이 같으므로 지수는 같아야 합니다:
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+ \\[\n8 = 2x\n\\]\n\n\\(x\\)를 구하면:
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+ \\[\nx = \\frac{8}{2} = 4\n\\]
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+ 검증:\n- \\(4^4 = 256\\)이고 \\(2^8 = 256\\)이므로 해가 맞음을 확인할 수 있습니다.
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+ 로그를 사용한 검증도 결과를 확인합니다.\n\n따라서, \\(x\\)의 값은 \\(\\boxed{4}\\)입니다.
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+ </solution>
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+ ```
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+ ## Citation
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+ ```
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+ @article{ko2025understand,
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+ title={Understand, Solve and Translate: Bridging the Multilingual Mathematical Reasoning Gap},
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+ author={Ko, Hyunwoo and Son, Guijin and Choi, Dasol},
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+ journal={arXiv preprint arXiv:2501.02448},
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+ year={2025}
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+ }
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+ ```