hainguyen306201/bank-model
The hainguyen306201/bank-model is a 4.0 billion parameter instruction-tuned language model, fine-tuned from Qwen/Qwen3-4B-Instruct-2507. This model is specifically designed and optimized for tasks related to banking and finance. It offers a context length of 262,144 tokens, making it suitable for processing extensive financial documents and queries.
Loading preview...
Overview
The hainguyen306201/bank-model is a specialized language model, fine-tuned from the Qwen/Qwen3-4B-Instruct-2507 base model. With 4.0 billion parameters and an extensive context length of 262,144 tokens, it is engineered to handle complex tasks within the banking and financial sectors.
Key Capabilities
- Domain-Specific Fine-tuning: Optimized for understanding and generating content relevant to banking and finance.
- Large Context Window: Supports processing of very long documents and conversations, crucial for financial analysis and customer service.
- Instruction Following: Inherits strong instruction-following capabilities from its Qwen3-4B-Instruct base, making it adaptable to various prompts.
- Customizable: Designed to be further fine-tuned for even more specific banking tasks, allowing for tailored applications.
Usage and Setup
This model comes with full configuration and tokenizer files. Users need to upload the model weights from the base model Qwen/Qwen3-4B-Instruct-2507 to enable full functionality. Both an automated script and a manual Python method are provided for this process. Once weights are set up, the model can be easily integrated using the transformers library for inference and further training.