README.md

---
library_name: transformers
tags: []
---

# Model Card for Meta-Llama-3-8B-for-bank

This model, **Meta-Llama-3-8B-for-bank**, is a fine-tuned version of the `meta-llama/Meta-Llama-3-8B-Instruct` model. It is optimized for financial service-related tasks, enabling users to interact with the model using natural language for common financial operations such as balance inquiries, retrieving stock lists, buying stocks, and performing deposit/withdrawal transactions.

## Model Details

### Model Description

- **Model Name**: Meta-Llama-3-8B-for-bank
- **Base Model**: `meta-llama/Meta-Llama-3-8B-Instruct`
- **Fine-tuning Data**: Custom financial chat examples
- **Version**: 1.0
- **License**: [Model License (if any)]
- **Language**: English

### Model Type

- **Architecture**: LLaMA-3
- **Type**: Instruction-based language model

### Model Usage

This model is designed for financial service tasks such as:

- **Balance Inquiry**:
  - *Example*: "Can you provide the current balance for my account?"
- **Stock List Retrieval**:
  - *Example*: "Can you provide me with a list of my stocks?"
- **Stock Purchase**:
  - *Example*: "I'd like to buy stocks worth $1,000.00 in Tesla."
- **Deposit Transactions**:
  - *Example*: "I'd like to deposit $500.00 into my account."
- **Withdrawal Transactions**:
  - *Example*: "I'd like to withdraw $200.00 from my account."
- **Transaction History**:
  - *Example*: "I would like to view my transactions. Can you provide it?"

### Inputs and Outputs

- **Inputs**: Natural language queries related to financial services.
- **Outputs**: Textual responses or actions based on the input query.

### Fine-tuning

This model has been fine-tuned with a dataset specifically created to simulate financial service interactions, covering a variety of questions related to account management and stock trading.

## Intended Use

This model is intended for integration into financial chatbots, virtual assistants, or other systems requiring automated handling of financial queries.

## Limitations

- **Domain Specificity**: The model may not perform well outside financial-related tasks.
- **Misinterpretation Risks**: There is a potential risk of misunderstanding complex or ambiguous queries.

## Ethical Considerations

- **Bias**: Trained on synthetic data, the model may not represent all user demographics.
- **Privacy**: The model should be used in compliance with financial privacy regulations.

## How to Use

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("jeromecondere/Meta-Llama-3-8B-for-bank")
model = AutoModelForCausalLM.from_pretrained("jeromecondere/Meta-Llama-3-8B-for-bank").to("cuda")

# Example of usage
name = 'Walter Sensei'
company = 'Amazon Inc.'
stock_value = 42.24
messages = [
    {'role': 'system', 'content': f'Hi {name}, I\'m your assistant how can I help you'},
    {"role": "user", "content": f"yo, can you just give me the balance of my account?"}
]

# Prepare the message using the chat template
res1 = tokenizer.apply_chat_template(messages, tokenize=False)
print(res1+'\n\n')

# Prepare the messages for the model
input_ids = tokenizer.apply_chat_template(messages, truncation=True, add_generation_prompt=True, return_tensors="pt").to("cuda")

# Inference
outputs = model.generate(
        input_ids=input_ids,
        max_new_tokens=100,
        do_sample=True,
        temperature=0.1,
        top_k=50,
        top_p=0.95
)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True)[0])