Gaurav1872/Qwen2.5-14B-Instruct

TEXT GENERATIONPricing:Input $0.431 / Cached $0.0862 / Output $1.12Concurrent Unit Cost:1Model Size:14.8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 14, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Gaurav1872/Qwen2.5-14B-Instruct is a 14.7 billion parameter instruction-tuned causal language model from the Qwen2.5 series, developed by Qwen Team. This model significantly improves capabilities in coding, mathematics, and instruction following, while also excelling at generating long texts and structured outputs like JSON. It supports a full context length of 131,072 tokens and multilingual generation across 29 languages, making it suitable for diverse and complex natural language processing tasks.

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Qwen2.5-14B-Instruct Overview

Qwen2.5-14B-Instruct is a 14.7 billion parameter instruction-tuned causal language model, part of the Qwen2.5 series developed by the Qwen Team. It builds upon previous Qwen models with substantial enhancements across several key areas. The model utilizes a transformer architecture featuring RoPE, SwiGLU, RMSNorm, and Attention QKV bias, and supports a full context length of 131,072 tokens for input and 8,192 tokens for generation.

Key Capabilities & Improvements

  • Enhanced Knowledge & Reasoning: Significantly improved capabilities in coding and mathematics, leveraging specialized expert models.
  • Instruction Following: Demonstrates better adherence to instructions and understanding of diverse system prompts, aiding in role-play and conditional chatbot implementations.
  • Long Text & Structured Data Handling: Excels at generating long texts (over 8K tokens) and understanding/generating structured data, including JSON outputs.
  • Multilingual Support: Offers robust support for over 29 languages, including major global languages like Chinese, English, French, Spanish, German, and Japanese.
  • Context Length: Features a full context length of 131,072 tokens, with techniques like YaRN available for handling extensive inputs beyond 32,768 tokens.

When to Use This Model

This model is particularly well-suited for applications requiring:

  • Advanced coding and mathematical problem-solving.
  • Precise instruction following and structured output generation (e.g., JSON).
  • Processing and generating very long texts.
  • Multilingual applications across a broad range of languages.
  • Chatbot implementations demanding resilient system prompt understanding and role-play capabilities.

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