shatu/Qwen3.5-2B-Reasoning-Fix
Qwen3.5-2B-Reasoning-Fix is a 2.3 billion parameter causal language model with a vision encoder developed by Qwen. This post-trained model integrates multimodal learning and an efficient hybrid architecture, featuring Gated Delta Networks and sparse Mixture-of-Experts. It is optimized for reasoning, coding, agent tasks, and visual understanding, offering expanded support for 201 languages and dialects. Intended for prototyping, task-specific fine-tuning, and research, it supports a native context length of 262,144 tokens.
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Qwen3.5-2B-Reasoning-Fix Overview
Qwen3.5-2B-Reasoning-Fix is a 2.3 billion parameter multimodal causal language model from the Qwen family, designed for high utility and performance. It features a unified vision-language foundation achieved through early fusion training, enabling strong performance across reasoning, coding, agent tasks, and visual understanding benchmarks. The model incorporates an efficient hybrid architecture combining Gated Delta Networks with sparse Mixture-of-Experts for high-throughput inference with minimal latency.
Key Capabilities
- Multimodal Learning: Excels in both language and vision-language tasks, outperforming previous Qwen3-VL models.
- Reasoning and Agentic Behavior: Demonstrates robust real-world adaptability through scalable reinforcement learning and strong tool-calling capabilities.
- Extensive Multilingual Support: Provides global linguistic coverage with support for 201 languages and dialects.
- Long Context Handling: Natively supports a context length of 262,144 tokens, beneficial for complex tasks.
When to Use This Model
This model is particularly well-suited for:
- Prototyping and Research: Its parameter scale makes it ideal for experimental development and academic studies.
- Task-Specific Fine-tuning: Can be fine-tuned for specialized applications requiring strong reasoning and multimodal understanding.
- Agent Applications: Recommended for building agent applications, especially with frameworks like Qwen-Agent and Qwen Code, due to its strong tool-calling features.
- Multilingual Applications: Excellent choice for applications requiring broad language support and nuanced cultural understanding.