Instructions to use vtava/Qwen3.5-0.8B-MemoryFusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vtava/Qwen3.5-0.8B-MemoryFusion with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("vtava/Qwen3.5-0.8B-MemoryFusion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Qwen3.5-0.8B × TinyCeNN Memory Fusion
Experimental accepted-only Memory Fusion adapter for Qwen/Qwen3.5-0.8B. Native Qwen3.5 Gated DeltaNet layers remain unchanged.
Target anchors: [3, 7, 11, 15, 19, 23]
Accepted anchors: [3, 7]
Base revision: 2fc06364715b967f1860aea9cf38778875588b17
Source commit: 3ac52ab6495f6872fdf5f50af4cb433e0542a2bc
Acceptance gates: NMSE ≤ 0.20, cosine ≥ 0.90, incremental ΔNLL ≤ 0.015, cumulative ΔNLL ≤ 0.05. chat_examples.json contains deterministic original-vs-Memory-Fusion chat tests. The unaccepted current layer, when present, is stored only under training/.
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