StockLLM is an open-source fine-tuned 1B large language model as the backbone of our first retrieval-augmented generation (RAG) framework specifically designed for financial time-series forecasting. Paper or resources for more information: https://arxiv.org/pdf/2502.05878 This repository and its contents are provided for academic and educational purposes only. None of the material constitutes financial, legal, or investment advice. No warranties, express or implied, are offered regarding the accuracy, completeness, or utility of the content. The authors and contributors are not responsible for any errors, omissions, or any consequences arising from the use of the information herein. Users…
Access requested at publisher
llama3.2
1.2B parameters
transformers
This is our first dedicated retriever for financial time-series forecasting, Financial TimeSeries Retriever (FinSeer). Paper or resources for more information: https://arxiv.org/pdf/2502.05878 The primary use of FinSeer is research on financial time-series forecasting using retrieval-augmented generation (RAG) framework. Install Package pip install InstructorEmbedding pip install -U FlagEmbedding pip install sentence-transformers==2.2.2 pip install protobuf==3.20.0 pip install yahoo-finance python -m pip install -U angle-emb pip install transformers==4.33.2 # UAE This repository and its contents are provided for academic and educational purposes only. None of the material constitutes…
Access requested at publisher
109M parameters
transformers
FinMA-7B-NLP is a financial large language model (LLM) developed as part of the PIXIU project. It is designed to understand complex financial language and concepts, and is fine-tuned to follow natural language instructions, enhancing its performance in downstream financial tasks. Specifically, FinMA-7B-NLP is trained only on the NLP tasks of the PIXIU dataset, making it specialized for tasks such as sentiment analysis, news headline classification, named entity recognition, and question answering. In addition to FinMA-7B-NLP, the PIXIU project includes two other models: FinMA-7B-full and FinMA-30B. You can use the FinMA-7B-NLP model in your Python project with the Hugging Face Transformers…
Access requested at publisher
mit
6.7B parameters
transformers
FinMA-7B-full is a comprehensive financial large language model (LLM) developed as part of the PIXIU project. It is designed to understand complex financial language and concepts, and is fine-tuned to follow natural language instructions, enhancing its performance in downstream financial tasks. Specifically, FinMA-7B-full is trained with the full instruction data from the PIXIU dataset, covering both NLP and prediction tasks. This makes it a more comprehensive model capable of handling a wider range of financial tasks. In addition to FinMA-7B-full, the PIXIU project includes two other models: FinMA-7B-NLP and FinMA-30B. You can use the FinMA-7B-full model in your Python project with the…
Access requested at publisher
mit
6.7B parameters
transformers