ertghiu256/Qwen3.5-2b-ReMix
ertghiu256/Qwen3.5-2b-ReMix is a 2.3 billion parameter, native Float16 fine-tune of Qwen/Qwen3.5-2B, developed by ertghiu256. This model is specifically optimized for advanced mathematical reasoning, logical deduction, and structured coding problems. It aims to deliver frontier-style reasoning capabilities on local consumer hardware, leveraging multi-source open-source distillation data.
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Qwen3.5-2B-ReMix: Reasoning-Focused 2B Model
This model, developed by ertghiu256, is a fully merged, native Float16 (F16) fine-tune of the Qwen/Qwen3.5-2B architecture. Its core objective is to significantly enhance performance on complex reasoning tasks, including advanced mathematics, logical deduction, and structured coding problems. The model achieves this by leveraging 100% open-source distilled reasoning datasets, ensuring no proprietary data was used in its training.
Key Capabilities & Features
- Base Architecture: Qwen/Qwen3.5-2B (Dense, Hybrid Gated DeltaNet).
- Precision: Native Float16 (F16) merged weights, requiring no external adapters.
- Primary Focus: Advanced mathematical reasoning and complex code generation/debugging.
- Training Data: Exclusively open-source distilled reasoning datasets.
- Target Environment: Designed for efficient local execution on consumer hardware.
- Optimized Generation: Provides recommended parameters for "Everyday Use" (balanced) and "Deep Reasoning" (deterministic, logical consistency).
Limitations
Users should be aware of potential hallucinations, inconsistent stylistic outputs, and occasional logic mismatches for highly niche programming or academic proofs, as is common with compact models pushing performance boundaries.