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@@ -123,7 +123,7 @@ To train the model, the data needs to be in the following format. Note the data
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  Once the data is in the correct format, QLoRA is recommended. The model can be fine-tuned either using mlx-lm and mps (to tune on an Apple Silicon machine) or a bitsandbytes configuration and cuda (to tune on a machine with Nvidia GPUs).
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- #### Preprocessing [optional]
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  To preprocess your data to be in the correct format outlined above, you can use the following helper function:
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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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- ## 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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  Once the data is in the correct format, QLoRA is recommended. The model can be fine-tuned either using mlx-lm and mps (to tune on an Apple Silicon machine) or a bitsandbytes configuration and cuda (to tune on a machine with Nvidia GPUs).
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+ #### Preprocessing
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  To preprocess your data to be in the correct format outlined above, you can use the following helper function:
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  ## Evaluation
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+ Testing loss and perplexity were the two metrics used to evaluate the Angora models. A summary of the results for all the different iteration models is included below.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ### Results
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+ | Number of iterations | Testing Loss | Perplexity |
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+ |:----------|:----------|:---------|
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+ |800 | 0.569 | 1.766 |
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+ | 1600 | 0.302 | 1.352 |
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+ | 2400 | 0.225 | 1.252 |
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+ | 3200 | 0.185 | 1.203 |
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+ | 4000 | 0.170 | 1.185 |
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ### Testing Data
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+ The testing data is available [here](https://huggingface.co/datasets/band2001/stolaf-angora/viewer/default/test).
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  ## Model Card Contact
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+ Ben Anderson - [ander6@stolaf.edu](mailto:ander6@stolaf.edu)
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+ Keegan Murray - murray7@stolaf.edu(mailto:murray7@stolaf.edu)