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
1from peft import PeftModel, PeftConfig
2from transformers import AutoModelForCausalLM, AutoTokenizer
3
4# Load the base model
5model_name = "unsloth/gemma-7b-bnb-4bit"
6tokenizer = AutoTokenizer.from_pretrained(model_name)
7model = AutoModelForCausalLM.from_pretrained(model_name)
8
9# Load the fine-tuned model
10peft_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]
