gemma_7b_finRisk_r6_3VersionQ

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license: mit

Model Card for shijunju/gemma_7b_finRisk_r6_3VersionQ

The repository includes only a LoRA adapter trained with keras-nlp on a TPU. To use the fine-tuned model, load Gemma-7b and apply load_lora_weights before generating output (see instructions below).

This model is fine-tuned using the LoRA (Low-Rank Adaptation) approach, specifically designed for question answering in the domain of financial risk compliance.

The Gemma-7b-en model is fine-tuned using documents from fincen.gov.

It is capable of answering questions about documents published on fincen.gov, including Alerts, Advisories, and Financial Trend Analysis reports since 2020.

Model Details

Model Description

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• The model is created as part of experiment to find better models, a more accurate (70%-78%) finetuned model can found at: shijunju/gemma_7b_finRisk_r6_4VersionQ
• Developed by: Shijun Ju
• Finetuned from model: Gemma-7b-en
• The model is finetuned using Keras_nlp with TPU adapting the approach used at https://www.kaggle.com/code/nilaychauhan/keras-gemma-distributed-finetuning-and-inference
• LoRA rank: 10
• Accuracy rate for unseen paraphrased questions is 67% for default setting and 73% using Beam Search of 3.

Dataset Used

shijunju/fincen_all_questions_5versions

How to Get Started with the Model

Use the code below to get started with the model. (Require TPU or large GPU!)

# https://www.kaggle.com/code/nilaychauhan/keras-gemma-distributed-finetuning-and-inference

import tensorflow_text as text

model_parallel = keras.distribution.ModelParallel(
    device_mesh=device_mesh, 
    layout_map=layout_map, 
    batch_dim_name="batch"
)

keras.distribution.set_distribution(model_parallel)
gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset("gemma_7b_en")


gemma_lm.backbone.enable_lora(rank=6)

gemma_lm.backbone.load_lora_weights(MODEL_LORA_WT_PATH) # the folder and file name you save the downloaded adaptor from this repository

def generate_response(prompt, max_length=256):
    outputs = gemma_lm.generate(prompt, 
                             max_length=max_length
                               )
    print(outputs)

inference_template = """<start_of_turn>user\nQuestion: {question}\n<end_of_turn>\n\n<start_of_turn>model\n"""

prompt = inference_template.format(
    question="Identify the specific fraudulent scheme highlighted in the FinCEN alert related to requests for convertible virtual currency payments.",
    response=""
)

print(generate_response(prompt))

If you encounter any errors, please refer to https://www.kaggle.com/code/nilaychauhan/keras-gemma-distributed-finetuning-and-inference for how to load the model into TPU properly.

Model Card Contact

[email protected]