FinChat-AI is an advanced AI-powered project designed to analyze financial markets using Natural Language Processing (NLP) techniques. It leverages cutting-edge transformer models, such as ChatGLM2-6B, fine-tuned with LoRA (Low-Rank Adaptation), to provide insights into financial data and sentiment.
- Sentiment Analysis: Accurately determines the sentiment (positive, neutral, negative) of financial news and market-related text.
- Pattern Recognition: Identifies technical patterns like the Inverted Head and Shoulders from textual market descriptions.
- Fine-Tuned LoRA Weights: The repository includes pre-trained LoRA weights for fine-tuning ChatGLM2-6B, enabling efficient deployment of the model with reduced computational overhead.
- Customizable Prompts: Users can input financial scenarios or market updates, and the model generates insightful analyses based on pre-trained knowledge and fine-tuning.
- Transformer-based Architecture: Uses Hugging Face’s
transformerslibrary for seamless interaction and deployment of models.
Finchat2.ipynb: The primary notebook demonstrating how to load the model, interact with it, and perform tasks such as sentiment analysis and pattern recognition.saved_model.zip: Contains fine-tuned LoRA weights, allowing users to replicate and further fine-tune the project on their datasets.README.md: This file provides an overview of the project, its features, and usage instructions.- Training Resources: Scripts for training with LoRA and integrating with Meta-LLaMA-2 architecture.
- Base Model: ChatGLM2-6B
- Fine-Tuning: Utilizes LoRA for efficient parameter updates during training.
- Training Dataset: Focused on financial data, market updates, and technical analysis descriptions.
- Frameworks:
Hugging Face TransformersPEFTfor LoRAPyTorchfor model deployment