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title: Financial Bot
emoji: 🚀
colorFrom: red
colorTo: green
sdk: gradio
sdk_version: 4.16.0
app_file: app.py
pinned: false
license: mit
This is the Inference module of a 3-part FTI feature-training-inference RAG-framework LLMOps course.
In this iteration, I've replaced Falcon 7B Instruct with the currently-SoTa (Jan '24) Mistral-7B-Instruct-v0.2,
fine-tuned using Unsloth on financial questions and answers generated with the help of GPT-4, quantized
and augmented with a 4bit QLoRa.
Prompt analysis and model registry is handled by Comet LLM, and finance news is pulled via an Alpaca API, processed
by Bytewax, and then sent as a vector embedding to Qdrant's serverless vector store. LangChain chains the prompt and
most relevant news article to provide answers with real-time finance information embedded within the output.
#TODO: Add citations to output to show end-user which article has been used to generate the output.
I have contributed to the original MIT licensed (ka-ching!) course which can be found here: https://medium.com/decoding-ml/the-llms-kit-build-a-production-ready-real-time-financial-advisor-system-using-streaming-ffdcb2b50714