README.md

---
library_name: transformers
language:
- en
pipeline_tag: text-generation
datasets:
- Rahulholla/stock-analysis
---

# Model Card for Model ID

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## Model Details

### Model Description

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

- **Developed by:** Dehaze
- **Funded by [optional]:** Dehaze
- **Model type:** Text-generation
- **Language(s) (NLP):** English
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** Mistral-7B-v0.1

### Model Sources [optional]

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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

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### Direct Use

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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.

### Downstream Use

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

Users can fine-tune the model for specific tasks related to stock market analysis or integrate it into larger systems for automated trading strategies, financial advisory services, or sentiment analysis of financial markets.

## Bias, Risks, and Limitations

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The model's predictions may be influenced by biases present in the training data, such as historical market trends or prevailing market sentiment. Additionally, the model's effectiveness may vary depending on the quality and relevance of the input data provided by users.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
Users should exercise caution and validate the model's predictions with additional research and analysis before making any trading decisions. It's also recommended to consider multiple sources of information and consult with financial experts when interpreting the model's output.

## How to Get Started with the Model

# Getting Started with the Model

## Installation

Ensure that you have the `transformers` library installed. If not, you can install it via pip:

```bash
pip install transformers

You can load the model using the provided pipeline or directly with the AutoTokenizer and AutoModelForCausalLM classes from the transformers library.
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.
Alternatively, you can directly interact with the model using the tokenizer and model objects as well.

## Training Details

### Training Data

<!-- 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. -->

The model was trained on a dataset containing examples of stock option data paired with corresponding trading insights. The dataset includes information such as implied volatility, option prices, technical indicators, and trading recommendations for various stocks.

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

The input data was preprocessed to tokenize and encode the text input before training.

#### Training Hyperparameters

- **Training regime:** 
Training regime: Mixed precision training with bf16 precision.
Warmup steps: 1
Per-device train batch size: 2
Gradient accumulation steps: 1
Max steps: 500
Learning rate: 2.5e-5
Optimizer: paged_adamw_8bit
Logging and saving strategy: Logging and saving checkpoints every 25 steps with wandb integration.

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

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The testing data consisted of examples similar to the training data, with stock option data and expected trading insights provided.

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

Factors considered during evaluation include the quality of the model's predictions, alignment with expected trading recommendations, and consistency across different test cases.

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

Evaluation metrics include accuracy of trading recommendations, relevance of generated insights, and overall coherence of the model's output.

### Results

The model demonstrated the ability to provide relevant and actionable trading insights based on the input stock option data.

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

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## Environmental Impact

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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).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### 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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**BibTeX:**

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**APA:**

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## Glossary [optional]

<!-- 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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