braindao/Qwen2.5-14B

TEXT GENERATIONConcurrency Cost:1Model Size:14.8BQuant:FP8Ctx Length:32kPublished:Mar 6, 2025License:apache-2.0Architecture:Transformer Open Weights Cold

braindao/Qwen2.5-14B is a 14.7 billion parameter base causal language model developed by Qwen, featuring a context length of 131,072 tokens. This model significantly improves upon its predecessor, Qwen2, with enhanced knowledge, coding, and mathematical capabilities, thanks to specialized expert models. It also offers improved instruction following, long text generation, structured data understanding, and multilingual support for over 29 languages, making it suitable for further fine-tuning for diverse applications.

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Qwen2.5-14B Overview

braindao/Qwen2.5-14B is a 14.7 billion parameter base causal language model from the latest Qwen2.5 series, developed by Qwen. It builds upon the Qwen2 architecture with significant enhancements across several key areas. This model is designed for pretraining and is not recommended for direct conversational use; instead, it serves as a robust foundation for further post-training, such as SFT or RLHF.

Key Capabilities

  • Enhanced Knowledge & Reasoning: Significantly improved capabilities in coding and mathematics, leveraging specialized expert models.
  • Instruction Following: Demonstrates substantial improvements in adhering to instructions and generating structured outputs, including JSON.
  • Long Context & Generation: Supports an extensive context length of up to 131,072 tokens and can generate texts up to 8,000 tokens.
  • Multilingual Support: Offers robust support for over 29 languages, including major global languages like Chinese, English, French, Spanish, German, and Japanese.
  • Structured Data Understanding: Better at understanding and processing structured data, such as tables.
  • System Prompt Resilience: More resilient to diverse system prompts, improving role-play and chatbot condition-setting.

Good for

  • Foundation for Fine-tuning: Ideal for developers looking to apply further supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), or continued pretraining for specific tasks.
  • Applications Requiring Long Context: Suitable for tasks that demand processing and generating very long texts.
  • Multilingual NLP Projects: Excellent for applications targeting a broad range of languages.
  • Code & Math-Intensive Tasks: A strong base for developing models focused on programming and complex mathematical problem-solving.

Finetunes
2 models