EmaRimoldi/MNLP_M2_rag_model
EmaRimoldi/MNLP_M2_rag_model is a 0.8 billion parameter language model, fine-tuned from Qwen/Qwen3-0.6B-Base. It was trained on 100 data points from the mathQA dataset, making it specialized for mathematical question answering tasks. The model utilizes a learning rate of 5e-5, a per-device train batch size of 1, and was trained for 1 epoch using the AdamW optimizer. Its 32768 token context length supports processing longer mathematical problems and related contexts.
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Model Overview
EmaRimoldi/MNLP_M2_rag_model is a specialized language model, fine-tuned from the Qwen/Qwen3-0.6B-Base architecture. With 0.8 billion parameters and a substantial 32768 token context length, this model is designed for specific applications requiring mathematical reasoning.
Key Capabilities
- Mathematical Question Answering: The model has been specifically trained on 100 data points from the mathQA dataset, indicating a focus on understanding and generating responses related to mathematical problems.
- Fine-tuned Performance: Leveraging a learning rate of 5e-5 and the AdamW optimizer, the model underwent a targeted training regimen over 1 epoch to adapt its capabilities to the mathQA domain.
Good For
- Math-centric Applications: Ideal for use cases involving mathematical problem-solving, educational tools, or systems that require processing and generating mathematical content.
- Research in Domain Adaptation: Provides a base for further experimentation and fine-tuning on similar specialized datasets, particularly within quantitative reasoning or technical domains.