unsloth/Qwen2.5-7B-Instruct
unsloth/Qwen2.5-7B-Instruct is a 7.61 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 excels in coding, mathematics, instruction following, and generating structured outputs like JSON. This model offers significant improvements in long text generation and multilingual support across 29 languages.
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Qwen2.5-7B-Instruct Overview
This model is an instruction-tuned variant of the Qwen2.5 series, developed by Qwen, featuring 7.61 billion parameters and a substantial 131,072 token context window. It builds upon the Qwen2 architecture with notable enhancements in several key areas.
Key Capabilities & Improvements
- Enhanced Knowledge & Reasoning: Significantly improved capabilities in coding and mathematics, leveraging specialized expert models.
- Instruction Following: Demonstrates stronger instruction following, better resilience to diverse system prompts, and improved role-play implementation.
- Long Text Generation: Excels at generating long texts (over 8K tokens) and understanding/generating structured data, including JSON.
- Multilingual Support: Offers robust support for over 29 languages, including Chinese, English, French, Spanish, German, and Japanese.
- Architecture: Utilizes a transformer architecture with RoPE, SwiGLU, RMSNorm, and Attention QKV bias.
- Long Context Processing: Supports up to 128K tokens, with a generation capacity of 8K tokens, and can be configured with YaRN for handling extensive inputs.
Good For
- Applications requiring strong coding and mathematical reasoning.
- Chatbots and agents needing robust instruction following and role-play capabilities.
- Tasks involving long document processing and summarization.
- Generating structured outputs such as JSON.
- Multilingual applications across a wide range of languages.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.