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