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app.py
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@@ -310,12 +310,14 @@ demo = gr.Interface(
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title="FinGPT-Forecaster",
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description="""FinGPT-Forecaster takes random market news and optional basic financials related to the specified company from the past few weeks as input and responds with the company's **positive developments** and **potential concerns**. Then it gives out a **prediction** of stock price movement for the coming week and its **analysis** summary.
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This model is finetuned on Llama2-7b-chat-hf with LoRA on the past year's DOW30 market data
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Company profile & Market news & Basic financials & Stock prices are retrieved using **yfinance & finnhub**.
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For more detailed and customized implementation, refer to our FinGPT project: <https://github.com/AI4Finance-Foundation/FinGPT>
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Setting do_sample=False or modifying the temperature during the generation process also helps stabilize the prediction result.
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**Disclaimer: Nothing herein is financial advice, and NOT a recommendation to trade real money. Please use common sense and always first consult a professional before trading or investing.**
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title="FinGPT-Forecaster",
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description="""FinGPT-Forecaster takes random market news and optional basic financials related to the specified company from the past few weeks as input and responds with the company's **positive developments** and **potential concerns**. Then it gives out a **prediction** of stock price movement for the coming week and its **analysis** summary.
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This model is finetuned on Llama2-7b-chat-hf with LoRA on the past year's DOW30 market data but **welcomes any ticker symbol**.
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Company profile & Market news & Basic financials & Stock prices are retrieved using **yfinance & finnhub**.
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For more detailed and customized implementation, refer to our FinGPT project: <https://github.com/AI4Finance-Foundation/FinGPT>
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This demo has been downgraded to using **T4 with 8-bit inference** due to cost considerations, speed & performance may be affected.
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⚠️Warning: This is just a demo showing what this model can do. During each individual inference, company news is **randomly sampled** from all the news from designated weeks, which might result in **different predictions for the same period**.
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We suggest users deploy the [original model](https://huggingface.co/FinGPT/fingpt-forecaster_dow30_llama2-7b_lora) or clone this space and inference with more carefully selected news in their favorable ways.
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Setting do_sample=False or modifying the temperature during the generation process also helps stabilize the prediction result.
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**Disclaimer: Nothing herein is financial advice, and NOT a recommendation to trade real money. Please use common sense and always first consult a professional before trading or investing.**
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