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@@ -46,7 +46,7 @@ This is the model card of a 🤗 transformers model that has been pushed on the
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  The model can be directly used to analyze stock option data and provide actionable trading insights based on the input provided. It can assist users in understanding key metrics such as implied volatility, option prices, technical indicators, and more, to make informed trading decisions.
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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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  ## 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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  The model can be directly used to analyze stock option data and provide actionable trading insights based on the input provided. It can assist users in understanding key metrics such as implied volatility, option prices, technical indicators, and more, to make informed trading decisions.
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+ ### Downstream Use
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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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  ## How to Get Started with the Model
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+ # Getting Started with the Model
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+ ## Installation
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+
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+ Ensure that you have the `transformers` library installed. If not, you can install it via pip:
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+ ```bash
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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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+ Alternatively, you can directly interact with the model using the tokenizer and model objects as well.
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  ## Training Details
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