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gandhiraketla277/
finance-llama-3.1-8b

Name: finance-llama-3.1-8b
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finance-llama-3.1-8b

Model Description

This is a fine-tuned version of Meta's Llama-3.1-8B model, specialized for financial question-answering and advice. The model has been trained on financial instruction data to provide better responses to finance-related queries.

Training Details

Base Model: meta-llama/Llama-3.1-8B
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Training Framework: Unsloth
Dataset: Josephgflowers/Finance-Instruct-500k (subset of 10,000 samples)
Training Time: ~2 hours
Final Training Loss: 0.9580

Training Configuration

Learning Rate: 2e-4
Batch Size: 2 (per device)
Gradient Accumulation Steps: 4
Max Steps: 100
LoRA Rank: 16
LoRA Alpha: 16

Intended Use

This model is designed for:

Financial question answering
Investment advice and guidance
Tax-related queries
Retirement planning discussions
General financial education

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("gandhiraketla277/finance-llama-3.1-8b")
tokenizer = AutoTokenizer.from_pretrained("gandhiraketla277/finance-llama-3.1-8b")

# Example usage
prompt = "User: What is the difference between a tax credit and a tax deduction?\n\nAssistant:"
inputs = tokenizer.encode(prompt, return_tensors="pt")

with torch.no_grad():
    outputs = model.generate(
        inputs,
        max_new_tokens=200,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response[len(prompt):])

Example Queries

The model can handle various financial topics:

Tax planning and deductions
Investment strategies and portfolio diversification
Retirement planning (401k, IRA, etc.)
Debt management
Financial terminology explanations

Limitations

This model provides general financial information and should not be considered as professional financial advice
Always consult with qualified financial advisors for personalized guidance
The model's knowledge is based on training data and may not reflect the most current financial regulations
Not suitable for high-stakes financial decisions without professional verification

Training Dataset

The model was fine-tuned on a subset of the Finance-Instruct-500k dataset, which contains instruction-following examples focused on financial topics.

Evaluation

The model shows improved performance on financial question-answering tasks compared to the base Llama-3.1-8B model, with more domain-specific knowledge and better structured responses.

Ethical Considerations

This model should complement, not replace, professional financial advice
Users should verify information and consult qualified professionals for important financial decisions
The model may reflect biases present in the training data

Technical Details

Architecture: Llama-3.1-8B with LoRA adapters
Precision: 4-bit quantization during training
Framework: Unsloth for efficient fine-tuning
Hardware: Trained on GPU with CUDA support

Citation

If you use this model, please cite:

bibtex

@misc{finance_llama_3.1_8b,
  title={finance-llama-3.1-8b: A Finance-Specialized Llama-3.1-8B Model},
  author={gandhiraketla277},
  year={2025},
  howpublished={\url{https://huggingface.co/gandhiraketla277/finance-llama-3.1-8b}}
}

Acknowledgments

Meta AI for the base Llama-3.1-8B model
Unsloth team for efficient fine-tuning framework
Finance-Instruct-500k dataset creators