prithivMLmods/Gaea-Opus-14B-Exp
Gaea-Opus-14B-Exp is a 14.8 billion parameter language model developed by prithivMLmods, based on the Qwen 2.5 architecture. It is specifically optimized for general-purpose reasoning, contextual understanding, and multi-step problem-solving, leveraging fine-tuning with long chain-of-thought reasoning and specialized datasets. The model supports a 32768-token context length for input and can generate up to 8K tokens in output, making it suitable for detailed and structured responses across over 29 languages.
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Gaea-Opus-14B-Exp: Enhanced Reasoning and Multilingual Capabilities
Gaea-Opus-14B-Exp is a 14.8 billion parameter model built on the Qwen 2.5 architecture, developed by prithivMLmods. It is specifically designed to significantly enhance the reasoning capabilities of 14B-parameter models, excelling in contextual understanding, logical deduction, and multi-step problem-solving. The model has been fine-tuned using a long chain-of-thought reasoning approach and specialized datasets to improve comprehension, structured responses, and conversational intelligence.
Key Capabilities
- Enhanced General Knowledge: Provides broad knowledge across various domains for accurate and coherent responses.
- Improved Instruction Following: Advanced understanding and execution of complex instructions, generating structured and coherent outputs.
- Versatile Adaptability: Resilient to diverse prompts and capable of handling a wide range of topics and conversation styles.
- Long-Context Support: Supports up to 128K tokens for input context and can generate up to 8K tokens in a single output.
- Multilingual Proficiency: Supports over 29 languages, including English, Chinese, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, and Arabic.
Intended Use Cases
- General-Purpose Reasoning: Assisting with logical reasoning, diverse question answering, and general knowledge problems.
- Educational and Informational Assistance: Providing explanations, summaries, and research-based responses.
- Conversational AI and Chatbots: Building intelligent agents requiring contextual understanding and dynamic response generation.
- Multilingual Applications: Supporting global communication, translations, and multilingual content generation.
- Structured Data Processing: Analyzing and generating structured outputs like tables and JSON.
- Long-Form Content Generation: Creating extended responses such as articles, reports, and guides while maintaining coherence.