AmberYifan/capsd-qwen3-sciweb-stackexchange-Qwen3-4B-Base-science_ppl_b8000_s0
The AmberYifan/capsd-qwen3-sciweb-stackexchange-Qwen3-4B-Base-science_ppl_b8000_s0 model is a fine-tuned 4 billion parameter Qwen3-4B-Base model, optimized for scientific text processing. It was trained on a specialized dataset combining scientific web content and StackExchange data, enhancing its performance on science-related natural language tasks. This model is designed for applications requiring robust understanding and generation within scientific domains, leveraging its 32768 token context length.
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Model Overview
This model, AmberYifan/capsd-qwen3-sciweb-stackexchange-Qwen3-4B-Base-science_ppl_b8000_s0, is a specialized variant of the Qwen3-4B-Base architecture, featuring 4 billion parameters and a 32768 token context length. It has undergone fine-tuning to enhance its capabilities specifically within scientific domains.
Key Characteristics
- Base Model: Fine-tuned from
Qwen/Qwen3-4B-Base. - Specialized Training Data: The model was fine-tuned on the
capsd_Qwen3-4B-Base-n80000-sciweb-stackexchange__mix_science_ppl_b8000_s0dataset, indicating a focus on scientific web content and StackExchange data. - Training Hyperparameters: Training involved a learning rate of 1e-05, a total batch size of 64, and a cosine learning rate scheduler over 1 epoch.
Intended Use Cases
This model is particularly well-suited for applications that involve processing, understanding, or generating text within scientific fields. Its fine-tuning on relevant datasets suggests improved performance on tasks such as:
- Scientific document analysis.
- Question answering in science and technology.
- Summarization of research papers.
- Content generation for scientific explanations.