cuong1692001/Terminal-data_processing
cuong1692001/Terminal-data_processing is an 8 billion parameter language model fine-tuned from Qwen/Qwen3-8B. This model specializes in data processing tasks, having been trained on the nemotron_easy_data_processing, nemotron_medium_data_processing, and nemotron_mixed_data_processing datasets. It is optimized for handling and manipulating structured and unstructured data, making it suitable for various data-centric applications. The model leverages a 32768 token context length to process extensive data inputs efficiently.
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Overview
cuong1692001/Terminal-data_processing is an 8 billion parameter language model, fine-tuned from the robust Qwen/Qwen3-8B architecture. This model is specifically designed and optimized for data processing tasks, leveraging its training on a combination of nemotron_easy_data_processing, nemotron_medium_data_processing, and nemotron_mixed_data_processing datasets.
Key Capabilities
- Specialized Data Processing: Fine-tuned for handling various data processing operations.
- Foundation Model: Built upon the Qwen3-8B base, inheriting its general language understanding capabilities.
- Context Length: Supports a substantial context window of 32768 tokens, enabling the processing of large data inputs.
Training Details
The model was trained with a learning rate of 1e-05, using a total batch size of 8 across 8 GPUs. The training procedure involved 2.0 epochs with an AdamW optimizer and a cosine learning rate scheduler. The development utilized Transformers 5.6.0, Pytorch 2.12.1+cu130, Datasets 4.0.0, and Tokenizers 0.22.2.
Good For
- Applications requiring specialized data manipulation.
- Tasks involving structured or unstructured data processing.
- Scenarios benefiting from a large context window for data analysis.