Raobotnix/Qwen-2.5-1.5B-Slang-Explainer-V1
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 27, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold
The Raobotnix/Qwen-2.5-1.5B-Slang-Explainer-V1 is a 1.5 billion parameter Qwen2.5-based causal language model, finetuned by Raobotnix. It is specifically optimized for explaining various concepts, including a wide range of slang terms, leveraging a 32768 token context length. This model was trained using Unsloth and Huggingface's TRL library, focusing on comprehensive explanation capabilities.
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Raobotnix/Qwen-2.5-1.5B-Slang-Explainer-V1 Overview
This model is a 1.5 billion parameter Qwen2.5-based language model developed by Raobotnix. It has been finetuned from unsloth/Qwen2.5-1.5B-Instruct with a focus on explanatory tasks, particularly for understanding and defining slang.
Key Capabilities
- Slang Explanation: Designed to interpret and explain a wide variety of slang terms and colloquialisms.
- General Explanations: Capable of providing explanations for diverse topics beyond just slang.
- Efficient Training: Finetuned using Unsloth and Huggingface's TRL library, enabling faster training.
- Qwen2.5 Architecture: Benefits from the robust architecture of the Qwen2.5 series.
Good For
- Understanding Informal Language: Ideal for applications requiring the interpretation of modern and informal language.
- Educational Tools: Can be integrated into tools that help users understand complex or niche terminology.
- Content Moderation: Potentially useful in identifying and explaining slang in user-generated content.
- Quick Explanations: Provides concise explanations for various inputs, making it suitable for interactive applications.