smartinez1/Llama-3.1-8B-FINLLM

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Model Details

Model Description

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Uses

Direct Use

from transformers import BitsAndBytesConfig, AutoModelForCausalLM, AutoTokenizer
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, pipeline
import logging
# Suppress all warnings
logging.getLogger("transformers").setLevel(logging.CRITICAL) #weird warning when using model for inference

# Check if CUDA is available
if torch.cuda.is_available():
    num_devices = torch.cuda.device_count()
    print(f"Number of available CUDA devices: {num_devices}")
    
    for i in range(num_devices):
        device_name = torch.cuda.get_device_name(i)
        print(f"\nDevice {i}: {device_name}")
else:
    print("CUDA is not available.")
# Specify the device (0 for GPU or -1 for CPU)
device = 0 if torch.cuda.is_available() else -1

config = PeftConfig.from_pretrained("smartinez1/Llama-3.1-8B-FINLLM")
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B")
model = PeftModel.from_pretrained(base_model, "smartinez1/Llama-3.1-8B-FINLLM")
# Load the tokenizer associated with the base model
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B")
# Define the unique padding token for fine-tuning
custom_pad_token = "<|finetune_right_pad_id|>"
tokenizer.add_special_tokens({'pad_token': custom_pad_token})
pad_token_id = tokenizer.pad_token_id

# Set up the text generation pipeline with the PEFT model, specifying the device
generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device)

# List of user inputs
user_inputs = [
    "Provide a link for Regulation A (Extensions of Credit by Federal Reserve Banks) law",
    "Define the following term: Insurance Scores.",
    "Expand the following acronym into its full form: ESCB.",
    "Provide a concise answer to the following question: Which countries currently have bilateral FTAs in effect with the U.S.?",
    """Given the following text, only list the following for each: specific Organizations, Legislations, Dates, Monetary Values, 
    and Statistics When can counterparties start notifying the national competent authorities (NCAs) of their intention to apply 
    the reporting exemption in accordance with Article 9(1) EMIR, as amended by Regulation 2019/834?""",
    "Provide a concise answer to the following question: What type of license is the Apache License, Version 2.0?"
]

# Define the prompt template
prompt_template = """Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{}

### Answer:
"""

# Loop over each user input and generate a response
for user_input in user_inputs:
    # Format the user input into the prompt
    prompt = prompt_template.format(user_input)

    # Generate a response from the model
    response = generator(prompt, max_length=200, num_return_sequences=1, do_sample=True)

    # Extract and clean up the AI's response
    response_str = response[0]['generated_text'].split('### Answer:')[1].strip()
    cut_ind = response_str.find("#")  # Remove extra information after the response
    response_str = response_str[:cut_ind].strip() if cut_ind != -1 else response_str

    # Display the AI's response
    print(f"User: {user_input}")
    print(f"AI: {response_str}")
    print("-" * 50)  # Separator for clarity

Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

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Training Hyperparameters

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Evaluation

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Results

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Summary

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Framework versions

• PEFT 0.13.2