NexusLLM: A Unified Fine-Tuning Framework and Automated Deployment Solution for Financial Intelligence

NexusLLM is a financial domain-specific fine-tuning framework built on LLaMA-Factory, designed to lower the barrier to developing domain-specific models. It offers modular repository management, preconfigured Supervised Fine-Tuning (SFT) workflows, and hyperparameters optimized for Qwen-2.5, covering scenarios such as financial sentiment analysis and market trend reasoning, providing an integrated solution for the development and deployment of financial intelligence models.

General large language models (such as GPT, Claude, Qwen) perform well in a wide range of tasks, but vertical domains like finance require understanding of professional terminology and industry knowledge. However, fine-tuning involves complex processes such as data preparation and hyperparameter tuning, which is a barrier for organizations without an ML team. NexusLLM was created to address this.

NexusLLM is a high-performance fine-tuning environment based on LLaMA-Factory, focusing on the financial intelligence domain. Core positioning:

• Domain: Financial Intelligence (sentiment analysis, trend reasoning, investment insights)
• Base Model: Qwen-2.5-7B (optimized for inference)
• Fine-tuning Method: SFT
• Management Tool: Modular nexus.py script

1. Built on LLaMA-Factory: Supports multiple fine-tuning methods (SFT/DPO/PPO), model architectures, and dataset formats.
2. Modular Repository: Through a segmented push subsystem, uses nexus.py to implement module management (e.g., init/push data/push config), enabling clear control over asset versions.
3. Preconfigured SFT: Includes dataset_info.json (linking financial datasets), Qwen-2.5 optimized hyperparameter configurations, and financial sentiment training data.

1. Financial Sentiment Analysis: Analyze earnings call sentiment, social media stock sentiment, and news headline signals.
2. Market Trend Reasoning: Predict trend direction, analyze support/resistance levels, and interpret technical indicators.
3. Automated Investment Insights: Generate research reports, perform portfolio risk analysis, and detect market anomalies.

Qwen-2.5 advantages:

1. Strong reasoning ability (math/logic)
2. Native understanding of Chinese financial texts
3. Apache 2.0 open source and commercializable
4. Active ecosystem Compared to Llama/Mistral, it is more suitable for Chinese financial scenarios.

Value: Demonstrates the path of domain AI engineering (selecting suitable models, encapsulating expert knowledge, modular management, automated deployment), which can be extended to medical/legal fields. Outlook: Currently only supports SFT; future plans include expanding to RLHF/DPO, multimodality (financial report charts), real-time data access, and balancing general capabilities with professional depth.