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  license: apache-2.0
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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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- This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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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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- - **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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- [More Information Needed]
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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 Needed]
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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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  license: apache-2.0
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  ---
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/63e087b6a98d931aa90c1b9c/li48jA1I2Sa_hs3yUDtas.png)
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+ # interlocked-DUS(iDUS)
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+ We attempted to improve the performance of the model by further minimizing the layer distance without significantly departing from the framework of DUS.
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+ 💻 GitHub Repository: https://github.com/gauss5930/iDUS
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+ ## Architectural Details
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+ We propose **interlocked-DUS(iDUS)** the variant of DUS!
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+ As you can see from the name, it does not connect the layers as a whole like DUS but divides into groups and merges them so that they interlock with each other.
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+ With this mechanism, iDUS more effectively reduces the layer distance that was important in DUS and has greater strength in processing.
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+ The figure above illustrates the overall framework of iDUS.
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+ ## Experiments
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+ We created variants of DUS called interlocked-DUS(iDUS) and conducted experiments to verify the effectiveness of them.
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+ - [**iDUS-1layer**](https://huggingface.co/Cartinoe5930/SOLAR-10.7B-iDUS-1layer): The layers used are taken from a base model like DUS, but when merging, one layer per model is merged alternately. This variant aims to solve the layer distance problem more effectively.
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+ - **iDUS-8layer(iDUS)**: The concept is similar to iDUS-1layer, but iDUS-8layer uses 8 layers as a standard and merges them alternately. This variant aims to solve layer distance and boost processing effectively.
 
 
 
 
 
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+ To understand the effectiveness of these variants, it was uploaded to the [HuggingFace Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) and its performance was evaluated as follows.
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+ |Model|ARC|HellaSwag|MMLU|TruthfulQA|Winogrande|GSM8K|Average|
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+ |[Llama2_init_Mistral](https://huggingface.co/Cartinoe5930/Llama2_init_Mistral)|60.07|83.3|64.09|42.15|78.37|37.91|60.98|
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+ |[SOLAR-10.7B-DUS-Implementation](https://huggingface.co/Cartinoe5930/SOLAR-DUS-implement)|**59.56**|81.18|**63.68**|**40.72**|**76.48**|26.99|58.1|
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+ |[iDUS-1layer](https://huggingface.co/Cartinoe5930/SOLAR-10.7B-iDUS-1layer)|27.73|26.65|24.91|48.58|49.17|0|29.51|
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+ |**iDUS(iDUS-8layer)**|59.3|**81.34**|63.22|40.62|76.24|**29.57**|**58.38**|
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+ As shown in the table above, iDUS-1layer has significantly lower performance, and iDUS-8layer is slightly better than the original DUS used in the SOLAR-10.7B.
 
 
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+ ## Discussion
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+ We were able to obtain the following analysis through the result of experiments with variants of iDUS.
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+ - The performance of iDUS-1layer showed that alternately merging one layer at a time to solve the layer distance problem, but instead, it caused the model to go in a strange direction.
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+ - On the other hand, the iDUS-8layer showed good performance, it seems to be because it solved the layer distance problem to some extent and allows the model to properly process the information through the placement of successive layers.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ As a result, it was confirmed that it is important to solve the layer distance problem, however, it is also important to place consecutive layers together to process information effectively.
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+ Taking all of these points into consideration, we propose iDUS, which shows improved performance over the original DUS.
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+ Due to a lack of computation resources, further pre-training could not be performed in the SOLAR-10.7B implementation and iDUS experiment, making a more detailed analysis impossible.
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+ We will leave this limitation for future projects.