---
base_model: unsloth/gemma-7b-bnb-4bit
library_name: peft
license: mit
---
# Model Card for Stock Advisor

This is a fine-tuned language model designed to provide stock market analysis and recommendations based on current market data and trends.

## Model Details

### Model Description

The Stock Advisor is a fine-tuned variant of the Gemma-7B model, optimized for providing stock market analysis and recommendations. The model has been trained to understand and analyze market trends, company performance metrics, and provide informed insights about stock investments.

- **Developed by:** Adeola Oladeji, Daniel Boadzie
- **Model type:** Language Model (Fine-tuned)
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** unsloth/gemma-7b-bnb-4bit

### Model Sources

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

## Uses

### Direct Use

The model can be used to:
- Analyze current stock market trends
- Provide investment recommendations based on market data
- Explain market movements and their potential implications
- Offer insights into company performance metrics

### Downstream Use

- Integration into financial advisory platforms
- Stock market analysis tools
- Investment research applications
- Personal finance management systems

### Out-of-Scope Use

This model should not be used for:
- Guaranteed financial returns predictions
- Real-time trading decisions without human oversight
- Personal financial advice without proper regulatory compliance
- As a sole source for investment decisions

## Bias, Risks, and Limitations

- The model's analysis is based on historical data and may not account for unexpected market events
- Market predictions are inherently uncertain and should not be taken as financial guarantees
- The model may have biases towards well-known stocks or markets where more training data was available
- Performance may vary during unusual market conditions or black swan events

### Recommendations

- Users should always combine the model's insights with professional financial advice
- The model's outputs should be one of many tools used in investment decision-making
- Regular evaluation of the model's performance against current market conditions is recommended
- Users should be aware of local financial regulations and compliance requirements

## How to Get Started with the Model

```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the base model
model_name = "unsloth/gemma-7b-bnb-4bit"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Load the fine-tuned model
peft_model = PeftModel.from_pretrained(model, "path_to_your_finetuned_model")
```

## Training Details

### Training Data

The model was fine-tuned on current stock market data including:
- Historical price movements
- Company financial reports
- Market news and analysis
- Trading volumes and patterns

[Specific dataset details needed]

### Training Procedure

#### Training Hyperparameters

- **Training regime:** 4-bit quantization with PEFT
- **Framework versions:** PEFT 0.13.2

## Evaluation

### Testing Data, Factors & Metrics

#### Testing Data
- Recent market data
- Out-of-sample stock performance
- Historical market events

#### Factors
- Market conditions (bull/bear markets)
- Sector-specific performance
- Company size and market cap
- Market volatility levels

#### Metrics
- Prediction accuracy
- Recommendation quality
- Analysis comprehensiveness
- Risk assessment accuracy

### Results

[Specific evaluation results needed]

## Environmental Impact

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Model Card Authors

- Adeola Oladeji
- Daniel Boadzie

## Model Card Contact

For questions and feedback about this model, please contact:
- Adeola Oladeji
- Daniel Boadzie

[Contact information needed]