zjunlp/OceanGPT-basic-4B-Instruct
OceanGPT-basic-4B-Instruct is a 4 billion parameter instruction-tuned causal language model developed by zjunlp, based on the Qwen3 architecture. It is specifically trained on a large English and Chinese dataset focused on ocean science, giving it specialized knowledge in marine-related tasks. With a 32768 token context length, this model is designed to serve as an expert in answering diverse questions within the ocean domain.
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OceanGPT-basic-4B-Instruct: A Specialized LLM for Ocean Science
OceanGPT-basic-4B-Instruct, developed by zjunlp, is a 4 billion parameter instruction-tuned large language model built upon the Qwen3 architecture. Its core differentiator is its specialized training on extensive English and Chinese datasets within the ocean domain, making it a dedicated expert for marine science tasks. The model boasts a substantial 32768 token context length, enabling it to process and generate detailed responses to complex queries.
Key Capabilities
- Domain-Specific Expertise: Optimized for understanding and generating content related to ocean science, marine biology, oceanography, and other related fields.
- Bilingual Support: Trained on both English and Chinese ocean-related data, allowing for versatile application in different linguistic contexts.
- Instruction Following: Designed to follow instructions for various marine knowledge-based questions.
Limitations
- Hallucination: Like many large language models, it may exhibit hallucination issues.
- Identity Bias: The model's identity may reflect characteristics of its base models (Qwen/MiniCPM/LLaMA/GPT series).
- Prompt Sensitivity: Output consistency can be influenced by variations in prompt tokens.
Good For
- Researchers and students in oceanography and marine science seeking quick answers or summaries.
- Applications requiring specialized knowledge about marine environments, phenomena, and data.
- Developing tools for educational purposes or information retrieval in the ocean domain.