skemessage/Qwen2.5-7B-Instruct-neuron

TEXT GENERATIONConcurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Apr 9, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Qwen2.5-7B-Instruct-neuron is a 7.61 billion parameter instruction-tuned causal language model developed by Qwen, based on the Qwen2.5 series. It features significant improvements in coding, mathematics, and instruction following, with enhanced capabilities for generating long texts and structured outputs like JSON. This model supports a 131,072-token context length and is multilingual, covering over 29 languages.

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Qwen2.5-7B-Instruct-neuron Overview

This model is an instruction-tuned variant from the Qwen2.5 series, developed by Qwen. It builds upon the Qwen2 architecture, incorporating transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias. With 7.61 billion parameters, it is designed for robust performance across various natural language processing tasks.

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 understanding diverse system prompts, beneficial for role-play and chatbot applications.
  • Long Context & Generation: Supports an extensive context length of up to 131,072 tokens and can generate texts up to 8,192 tokens. It utilizes YaRN for efficient long-text processing.
  • Structured Data & Output: Excels at understanding structured data, such as tables, and generating structured outputs, particularly JSON.
  • Multilingual Support: Offers support for over 29 languages, including major global languages like Chinese, English, French, Spanish, German, and Japanese.

When to Use This Model

  • Complex Coding & Math Tasks: Ideal for applications requiring strong performance in programming and mathematical problem-solving.
  • Long-form Content Generation: Suitable for generating extensive texts or processing large documents due to its impressive context window.
  • Structured Data Processing: Effective for tasks involving the extraction or generation of structured information, including JSON outputs.
  • Multilingual Applications: A strong candidate for global applications needing support for a wide array of languages.
  • Robust Chatbots: Its resilience to diverse system prompts and improved instruction following make it well-suited for developing sophisticated chatbots and conversational AI.

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