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

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]

deotech
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StocK_Advisor