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title: Financial Bot | |
emoji: π | |
colorFrom: red | |
colorTo: green | |
sdk: gradio | |
sdk_version: 4.16.0 | |
app_file: app.py | |
pinned: false | |
license: mit | |
# Friendly Fincancial Bot | |
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] |