Mahesh111000/hanabi-qwen3-8b-rl-step150

TEXT GENERATIONPricing:Input $0.468 / Output $1.82Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 22, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

Mahesh111000/hanabi-qwen3-8b-rl-step150 is an 8.2 billion parameter causal language model from the Qwen3 series, developed by Qwen. This model uniquely supports seamless switching between a 'thinking mode' for complex reasoning, math, and coding, and a 'non-thinking mode' for efficient general-purpose dialogue. It demonstrates enhanced reasoning capabilities, superior human preference alignment for creative writing and multi-turn dialogues, and strong agent capabilities with external tool integration. The model also supports over 100 languages and dialects, with a native context length of 32,768 tokens, extendable to 131,072 tokens using YaRN.

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Qwen3-8B: A Versatile LLM with Dynamic Thinking Modes

Mahesh111000/hanabi-qwen3-8b-rl-step150 is an 8.2 billion parameter model from the Qwen3 series, designed to offer advanced reasoning and conversational capabilities. A key differentiator is its ability to seamlessly switch between a 'thinking mode' and a 'non-thinking mode' within a single model, optimizing performance for diverse tasks.

Key Capabilities:

  • Dynamic Reasoning: Excels in complex logical reasoning, mathematics, and code generation by engaging a dedicated 'thinking mode'. This mode is recommended for tasks requiring deep analytical processing.
  • Human Preference Alignment: Delivers superior performance in creative writing, role-playing, and multi-turn dialogues, providing a more natural and engaging conversational experience.
  • Agentic Functionality: Demonstrates strong capabilities in integrating with external tools, achieving leading performance among open-source models for agent-based tasks.
  • Multilingual Support: Supports over 100 languages and dialects, offering robust multilingual instruction following and translation.
  • Extended Context Window: Natively handles up to 32,768 tokens, with support for up to 131,072 tokens using the YaRN method for long text processing.

When to Use This Model:

  • Complex Problem Solving: Ideal for applications requiring detailed step-by-step reasoning, such as mathematical problems or code generation, by leveraging its 'thinking mode'.
  • Interactive Applications: Suitable for chatbots, creative writing assistants, and role-playing scenarios due to its strong human preference alignment.
  • Tool-Augmented Systems: Excellent for agent-based applications that need to interact with external tools for information retrieval or task execution.
  • Multilingual Deployments: A strong candidate for global applications requiring robust performance across many languages.

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