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Update README.md
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README.md
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Ensure that you have the `transformers` library installed. If not, you can install it via pip:
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```
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pip install transformers
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You can load the model using the provided pipeline or directly with the AutoTokenizer and AutoModelForCausalLM classes from the transformers library.
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Once the model is loaded, you can use it for text generation tasks. If you prefer a high-level interface, you can use the pipeline approach as well.
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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
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The input data was preprocessed to tokenize and encode the text input before training.
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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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[More Information Needed]
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#### Software
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[More Information Needed]
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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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[More Information Needed]
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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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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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Ensure that you have the `transformers` library installed. If not, you can install it via pip:
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```pip install transformers```
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You can load the model using the provided pipeline or directly with the AutoTokenizer and AutoModelForCausalLM classes from the transformers library.
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Once the model is loaded, you can use it for text generation tasks. If you prefer a high-level interface, you can use the pipeline approach as well.
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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
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The input data was preprocessed to tokenize and encode the text input before training.
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#### Summary
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## Technical Specifications
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### Model Architecture and Objective
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### Compute Infrastructure
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1 x A100 GPU - 80GB VRAM
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117 GB RAM
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12 vCPU
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