QwQ-35B-Eureka-Cubed
| Property | Value |
|---|---|
| Base Model | QwQ-32B |
| Parameter Count | ~35B parameters |
| Format | SafeTensors (supports GGUF, GPTQ, EXL2, AWQ, HQQ) |
| Required Template | ChatML |
What is QwQ-35B-Eureka-Cubed?
QwQ-35B-Eureka-Cubed is an enhanced version of the QwQ-32B model, incorporating additional capabilities through the innovative "Cubed" method. This model maintains all the exceptional abilities of QwQ-32B while adding augmentations from TinyR1-32b-preview and DeepSeek-R1-Distill-Qwen-32B. The model features multiple conclusion layers in series, adding approximately 2 billion parameters to enhance reasoning and output generation capabilities.
Implementation Details
The model employs a unique architecture that uses the "Cubed" method to multiply reasoning and output abilities. It requires the ChatML template without a system prompt and performs optimally with specific parameter settings: Temperature range 0.4-0.8, Repetition penalty 1.02-1.1, TopK 40, TopP 0.95, and MinP 0.05.
- Context window: Recommended minimum 4K, optimal at 8K+
- Unique ability to exceed context limits without breaking coherence
- Multiple conclusion layers integrated from different models
- Enhanced reasoning capabilities with decreased problem-solving time
Core Capabilities
- Superior reasoning and thinking abilities exceeding other models in its class
- Enhanced output quality and creativity for both scientific and creative tasks
- Improved brainstorming capabilities
- Ability to handle extended outputs (record: 12k coherent output with 4k context)
- Exceptional instruction following and comprehension
Frequently Asked Questions
Q: What makes this model unique?
The model's distinctive feature is its "Cubed" method, which combines multiple conclusion layers from different models, enhancing both reasoning power and output quality. It maintains QwQ-32B's exceptional abilities while adding capabilities from other powerful models.
Q: What are the recommended use cases?
The model is designed for all use cases, excelling particularly in tasks requiring deep reasoning, creative writing, and scientific analysis. It's especially effective for long-form content generation and complex problem-solving scenarios.
Q: Are there any known limitations?
The model occasionally generates Chinese tokens/symbols, and while it can exceed context limits without breaking, high temperatures (1+ or higher) may modify reasoning, output, and response style.
