unsloth/Qwen2.5-72B-Instruct
unsloth/Qwen2.5-72B-Instruct is a 72.7 billion parameter instruction-tuned causal language model developed by Qwen, based on the Qwen2.5 architecture. It features a 131,072 token context length and is significantly improved in coding, mathematics, instruction following, and generating long texts. This model excels at understanding structured data and producing structured outputs like JSON, with robust multilingual support for over 29 languages.
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Qwen2.5-72B-Instruct: Enhanced Performance and Long Context
This model is the instruction-tuned 72.7 billion parameter version of the Qwen2.5 series, developed by Qwen. It builds upon the Qwen2 architecture with substantial improvements across several key areas, making it a powerful tool for diverse applications.
Key Capabilities
- Enhanced Knowledge & Reasoning: Significantly improved capabilities in coding and mathematics, leveraging specialized expert models.
- Instruction Following & Generation: Offers better instruction following, improved generation of long texts (up to 8K tokens), and enhanced understanding of structured data (e.g., tables).
- Structured Output: Excels at generating structured outputs, particularly JSON, and is more resilient to varied system prompts for role-play and chatbot conditioning.
- Extended Context Length: Supports a long context window of up to 131,072 tokens and can generate up to 8,192 tokens, utilizing YaRN for efficient long-text processing.
- Multilingual Support: Provides robust support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, and Arabic.
Good For
- Applications requiring strong coding and mathematical reasoning.
- Tasks involving complex instruction following and structured data processing.
- Generating long-form content or structured outputs (e.g., JSON).
- Multilingual applications needing broad language coverage.
- Scenarios where long context understanding is critical.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.