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khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Slerp
Qwen3-4B-Qwen3.6-plus-Reasoning-Slerp is a text generation model from khazarai. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
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From the Hugging Face model README

Note: The sharp drop in "Creative Writing" is an expected and accepted trade-off to maximize extreme logical reasoning and coding precision.
This model is a highly experimental and optimized reasoning model created through a surgical SLERP merge of two powerful 4B reasoning models. The goal of this merge was to combine the deep analytical capabilities of Kimi with the mathematical and structural precision of Qwen, while mitigating the catastrophic forgetting commonly seen in SFT model merges. After multiple iterations and layer-by-layer tensor analysis, we achieved a "1+1=3 Synergy Effect" in Logical Inference and Planning, outperforming both base models and the official Qwen Thinking model.
Standard SLERP merges often destroy RAG capabilities and syntax adherence. To solve this, this model utilizes a custom merge configuration:
embed_tokens and lm_head are strictly pinned to 1.0 (Qwen). The model reads and speaks purely using Qwen's vocabulary, completely eliminating the RAG degradation problem.[0, 0.1, 0.2, 0.3, 0.5, 0.7, 0.8, 0.9, 1] to prevent weight interference in deep reasoning steps.| Model | Score |
|---|---|
| khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Slerp | 77.18 |
| khazarai/Qwen3-4B-Kimi2.5-Reasoning-Distilled | 76.09 |
| khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled | 75.64 |
| Qwen/Qwen3-4B-Thinking-2507 | 73.73 |
The following models were included in the merge:
The following YAML configuration was used to produce this model:
models:
- model: khazarai/Qwen3-4B-Kimi2.5-Reasoning-Distilled
- model: khazarai/Qwen3-4B-Qwen3.6-plus-Reasoning-Distilled
merge_method: slerp
base_model: khazarai/Qwen3-4B-Kimi2.5-Reasoning-Distilled
parameters:
t:
- filter: embed_tokens
value: 1
- filter: lm_head
value: 1
- value: 1
- filter: self
value: [0, 0.1, 0.2, 0.3, 0.5, 0.7, 0.8, 0.9, 1]
dtype: bfloat16