unsloth/Qwen3-235B-A22B-Instruct-2507
The Qwen3-235B-A22B-Instruct-2507 model by Qwen is a 235 billion parameter causal language model with 22 billion activated parameters and a native context length of 262,144 tokens. This updated instruction-tuned version significantly enhances general capabilities including instruction following, logical reasoning, mathematics, coding, and tool usage. It also shows substantial gains in long-tail knowledge coverage across multiple languages and improved alignment for subjective and open-ended tasks. Optimized for non-thinking mode, it excels in complex reasoning and agentic applications.
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Qwen3-235B-A22B-Instruct-2507: Enhanced Instruction-Following MoE Model
This model is an updated version of the Qwen3-235B-A22B series, specifically designed for instruction-following without a 'thinking mode'. It features a total of 235 billion parameters with 22 billion activated parameters and a native context length of 262,144 tokens, making it suitable for extensive long-context understanding.
Key Capabilities and Enhancements
- General Capabilities: Significant improvements across instruction following, logical reasoning, text comprehension, mathematics, science, coding, and tool usage.
- Multilingual Knowledge: Substantial gains in long-tail knowledge coverage across various languages.
- User Alignment: Markedly better alignment with user preferences for subjective and open-ended tasks, leading to more helpful and higher-quality text generation.
- Agentic Use: Excels in tool-calling capabilities, with recommendations to use Qwen-Agent for optimal performance.
- Performance Benchmarks: Demonstrates strong performance across various benchmarks, often outperforming its predecessor and competing models in categories like GPQA, AIME25, HMMT25, ARC-AGI, ZebraLogic, LiveCodeBench, and MultiIF.
When to Use This Model
This model is ideal for applications requiring:
- Advanced Instruction Following: For tasks where precise adherence to instructions is critical.
- Complex Reasoning: Excels in mathematical, scientific, and logical reasoning challenges.
- Long-Context Processing: Its 262K context window is beneficial for tasks requiring deep understanding of extensive documents or conversations.
- Agent-Based Systems: Strong tool-calling abilities make it suitable for integrating with external tools and building sophisticated agents.
- Multilingual Applications: Improved knowledge coverage across multiple languages supports diverse global use cases.