Sold by: BitextÂ
Overview
This model, "Mistral-7B-Retail-Banking-v1", is a fine-tuned version of the "Mistral-7B-Instruct-v0.2" model, specifically tailored for the Retail Banking domain. It is optimized to answer questions and assist users with various banking transactions. It has been trained using hybrid synthetic data generated using our NLP/NLG technology and our automated Data Labeling (DAL) tools.
The goal of this model is to show that a generic verticalized model makes customization for a final use case much easier. For example, if you are "ACME Bank", you can create your own customized model by using this fine-tuned model and doing an additional fine-tuning using a small amount of your own data. An overview of this approach can be found at https://www.bitext.com/two-step/Â
Highlights
- Intended Use: - Recommended applications: This model is designed to be used as the first step in Bitext’s two-step approach to fine-tuning LLMs for the Retail Banking domain, providing customers with fast and accurate answers about their banking needs. - Out-of-scope: It should not be used for non-banking related inquiries or for providing advice on medical, legal, or critical safety issues.
- The model was trained using a dataset designed for Retail Banking interactions, now publicly available on Hugging Face at https://huggingface.co/datasets/bitext/Bitext-retail-banking-llm-chatbot-training-dataset. This dataset comprises 26 different intents such as check_balance, transfer_money, open_account, and more, each with around 1000 examples.
- The training dataset includes: 25,545 question/answer pairs 4.98 million tokens 1224 entity/slot types Each entry consists of: Instruction: User request Category: High-level semantic category Intent: Specific intent of the user request Response: Example response from a virtual assistant The dataset covers a wide range of banking-related categories such as ACCOUNT, ATM, CARD, CONTACT, FEES, FIND, LOAN, PASSWORD, and TRANSFER, ensuring comprehensive training for handling diverse retail banking queries.
Details
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Pricing
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Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.2xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.2xlarge instance type, real-time mode | $0.00 |
ml.p3.2xlarge Inference (Batch) Recommended | Model inference on the ml.p3.2xlarge instance type, batch mode | $0.00 |
ml.g4dn.4xlarge Inference (Real-Time) | Model inference on the ml.g4dn.4xlarge instance type, real-time mode | $0.00 |
ml.p3.2xlarge Inference (Real-Time) | Model inference on the ml.p3.2xlarge instance type, real-time mode | $0.00 |
ml.g4dn.8xlarge Inference (Real-Time) | Model inference on the ml.g4dn.8xlarge instance type, real-time mode | $0.00 |
ml.g5.xlarge Inference (Real-Time) | Model inference on the ml.g5.xlarge instance type, real-time mode | $0.00 |
ml.g4dn.xlarge Inference (Real-Time) | Model inference on the ml.g4dn.xlarge instance type, real-time mode | $0.00 |
ml.g4dn.2xlarge Inference (Real-Time) | Model inference on the ml.g4dn.2xlarge instance type, real-time mode | $0.00 |
ml.g5.4xlarge Inference (Real-Time) | Model inference on the ml.g5.4xlarge instance type, real-time mode | $0.00 |
ml.p3.8xlarge Inference (Batch) | Model inference on the ml.p3.8xlarge instance type, batch mode | $0.00 |
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Delivery details
Amazon SageMaker model
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Version release notes
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Additional details
Inputs
- Summary
The model accepts text requests which specifies the user query.
- Input MIME type
- text/plain, application/json
<s>[INST] I want to transfer money, can you help me make a bank transfer? [/INST]
https://github.com/bitext/bitext-mistral-7b-banking/blob/main/examples/sample_input.txt
Support
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Please write to marketing@bitext.com to contact our support team. We are available Monday to Friday, 10am to 7pm PST. For queries during this time we will revert within 4 hours. For queries beyond this time, we would revert back in 8hours.
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