| { | |
| "id": "cachenotes_compressor", | |
| "name": "CacheNotes: Task-Aware Key-Value Cache Compression and Static Weight Compre", | |
| "file": "Codes/framework/compression_engine/cachenotes_compressor.py", | |
| "type": "Compression Engine", | |
| "category": "Low-Rank SVD & Factorization", | |
| "paradigm": "svd", | |
| "icon": "\u26a1", | |
| "description": "CacheNotes: Task-Aware Key-Value Cache Compression and Static Weight Compression with Learnable Continuous CPT (Compression-Planning-Token) Generator. Given a task description, CacheNotes generates Compression-Planning-Tokens (CPTs) that guide the compression of the corpus and KV cache into compact, reusable states. Scientific Enhancement: - Replaces static keyword heuristics with a continuous,", | |
| "classes": [ | |
| "CacheNotesLoRALinear", | |
| "CompressionPlanningTokens", | |
| "CPTGenerator", | |
| "CacheNotesKVCache", | |
| "CacheNotesCompressor" | |
| ], | |
| "methods_count": 20, | |
| "math_latex": "W_{\\text{cpt}} = \\operatorname{diag}(I_{\\text{task}}) W \\in \\mathbb{R}^{d_{\\text{out}} \\times d_{\\text{in}}}", | |
| "components": [ | |
| { | |
| "id": "v_factor", | |
| "name": "Factor V (Low-Rank Subspace Projection)", | |
| "role": "Projection to Rank r", | |
| "shape_transform": "[r \u00d7 d_in]", | |
| "color": "#3acbb8", | |
| "math_latex": "z_t = x_t V_r^\\top, \\quad V_r \\in \\mathbb{R}^{r \\times d_{in}}" | |
| }, | |
| { | |
| "id": "u_factor", | |
| "name": "Factor U (Full-Rank Expansion Projection)", | |
| "role": "Expansion to Output Space", | |
| "shape_transform": "[d_out \u00d7 r]", | |
| "color": "#2dd4bf", | |
| "math_latex": "y_t = z_t (\\Sigma_r U_r^\\top) + b, \\quad U_r \\in \\mathbb{R}^{d_{out} \\times r}" | |
| }, | |
| { | |
| "id": "adapter", | |
| "name": "Residual LoRA / Norm Calibration", | |
| "role": "Fidelity Compensation", | |
| "shape_transform": "[d_out \u00d7 d_in] Residual", | |
| "color": "#5eead4", | |
| "math_latex": "\\Delta y_t = x_t (B A)^\\top \\cdot \\frac{\\alpha}{r}, \\quad A \\in \\mathbb{R}^{r \\times d_{in}}, \\; B \\in \\mathbb{R}^{d_{out} \\times r}" | |
| } | |
| ], | |
| "model_projections": { | |
| "distilbert": { | |
| "hidden_size": 768, | |
| "intermediate_size": 3072, | |
| "baseline_layer_params": 6488064, | |
| "compressed_layer_params": 786432, | |
| "savings_ratio": 8.25 | |
| }, | |
| "roberta": { | |
| "hidden_size": 768, | |
| "intermediate_size": 3072, | |
| "baseline_layer_params": 6488064, | |
| "compressed_layer_params": 786432, | |
| "savings_ratio": 8.25 | |
| }, | |
| "gpt2": { | |
| "hidden_size": 768, | |
| "intermediate_size": 3072, | |
| "baseline_layer_params": 6488064, | |
| "compressed_layer_params": 786432, | |
| "savings_ratio": 8.25 | |
| }, | |
| "qwen": { | |
| "hidden_size": 896, | |
| "intermediate_size": 4864, | |
| "baseline_layer_params": 11124736, | |
| "compressed_layer_params": 1081344, | |
| "savings_ratio": 10.29 | |
| } | |
| } | |
| } |
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