lainlives/exp-da2
lainlives/exp-da2 is a 7.6 billion parameter instruction-tuned language model based on Qwen2.5-7B-Instruct-1M, developed by lainlives. It is fine-tuned with Claude Opus 4.5, Gemini, and GPT5.2 high-reasoning datasets to enhance its 'thinking' and reasoning capabilities, producing compact and focused reasoning blocks. This model excels at generating high-quality, detailed, and complex outputs by integrating an internal reasoning engine, making it suitable for tasks requiring deep thought and structured problem-solving.
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lainlives/exp-da2: Enhanced Reasoning Model
lainlives/exp-da2 is a 7.6 billion parameter model built upon the Qwen2.5-7B-Instruct-1M architecture, significantly enhanced for advanced reasoning and 'thinking' processes. This model integrates fine-tuning datasets from Claude Opus 4.5, Gemini, and GPT5.2, specifically targeting high-reasoning capabilities.
Key Capabilities
- Advanced Reasoning: Generates compact and 'to the point' reasoning blocks that precede and inform the final output, leading to higher quality, detail, and complexity in generations.
- Robust Output Quality: The integrated thinking engine directly improves the overall quality of generated text, making it suitable for intricate tasks.
- Temperature Flexibility: Reasoning activation is not affected by temperature settings, allowing for creative outputs with higher temperatures (e.g., 1.2+) while maintaining reasoning integrity.
- Context Length: Supports a substantial context window of 131,072 tokens, with a suggested minimum of 4k, ideally 8k+ for optimal performance.
- Prompt Activation: Can be prompted to activate deeper thinking by prepending "Think Deeply: " to the prompt if internal thinking doesn't self-generate.
Good For
- Complex Problem Solving: Ideal for use cases requiring structured thought processes and detailed, high-quality responses.
- Creative Generation: Excels in creative tasks when higher temperatures are applied, without compromising its reasoning foundation.
- Applications requiring 'Thinking' Blocks: Designed for scenarios where an explicit, internal reasoning process enhances the final output's coherence and depth.
Note: The model performs best with suggested settings (Temp .7+, Rep Pen 1.05, Topp .95, Minp .05, Topk 40) and recommends Q4KS or IQ3_M quants or higher for optimal reasoning activation.