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README.md
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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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- **Developed by:** Jaeyeol Choi, Yuchan Jung
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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):** English
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** https://huggingface.co/google/gemma-2-2b
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### Model Sources [optional]
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- **Repository:** https://github.com/YuchanJung/AI-Instructor
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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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<!-- 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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### 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:**
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#### Speeds, Sizes, Times [optional]
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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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[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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[More Information Needed]
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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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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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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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[More Information Needed]
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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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<!-- 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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- **Developed by:** Jaeyeol Choi, Yuchan Jung
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- **Model type:** LLM
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- **Language(s) (NLP):** English
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- **Finetuned from model [optional]:** https://huggingface.co/google/gemma-2-2b
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** https://github.com/YuchanJung/AI-Instructor
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## Uses
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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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https://huggingface.co/datasets/jaeyeol816/ai_lecture
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### Training Procedure
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https://github.com/YuchanJung/AI-Instructor?tab=readme-ov-file#model-training
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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:** Basic <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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### Results
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https://github.com/YuchanJung/AI-Instructor?tab=readme-ov-file#results
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#### Summary
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This project focuses on building an AI Instructor, a Q&A bot, using transcripts from the Andrew Ng's Deep Learning course. It was created specifically for the juniors of the Google ML Bootcamp to provide them with an interactive tool to deepen their understanding of key machine learning concepts. The provided model was created by fine-tuning the Gemma-2B model on a custom-generated Q&A dataset derived from the lecture content.
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## Model Card Contact
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