SirZQ/VeraRetouch_llava_Qwen2

TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:0.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 20, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

SirZQ/VeraRetouch_llava_Qwen2 is an instruction-tuned 0.5 billion parameter causal language model from the Qwen2.5 series, developed by Qwen. This model features a 32,768 token context length and is designed with transformers architecture including RoPE, SwiGLU, and RMSNorm. It offers significant improvements in coding, mathematics, instruction following, long text generation, and structured data understanding, making it suitable for diverse chatbot and data processing applications.

Loading preview...

Model Overview

SirZQ/VeraRetouch_llava_Qwen2 is an instruction-tuned variant of the Qwen2.5 series, developed by Qwen. This model, with 0.5 billion parameters, is built on a transformer architecture incorporating RoPE, SwiGLU, and RMSNorm. It supports a substantial context length of 32,768 tokens, with generation capabilities up to 8,192 tokens.

Key Capabilities

  • Enhanced Knowledge & Reasoning: Significantly improved performance in coding and mathematics, leveraging specialized expert models.
  • Instruction Following: Demonstrates stronger instruction adherence and resilience to diverse system prompts, beneficial for role-play and conditional chatbot scenarios.
  • Long Text & Structured Output: Excels at generating long texts (over 8K tokens) and understanding/generating structured data, including JSON.
  • Multilingual Support: Provides support for over 29 languages, including major global languages like Chinese, English, French, Spanish, and Japanese.

Ideal Use Cases

  • Chatbot Development: Its robust instruction following and resilience to system prompts make it suitable for creating interactive and context-aware chatbots.
  • Code Generation & Math Problem Solving: Improved capabilities in these domains make it useful for developer tools and educational applications.
  • Data Processing: Strong understanding of structured data and ability to generate structured outputs (like JSON) can be leveraged for data extraction and transformation tasks.
  • Multilingual Applications: Its broad language support enables development of applications catering to a global user base.

Finetunes
1 models