ZacharyZhang-NY
0ee99e271d
Real-models implementation: multimodal ingestion, Context Cartridge memory, sparse + Qwen embedding retrieval, Cache Fabric, Qwen runtime adapters, verifier contracts, Visual HashHop proofs, BF16 LoRA fine-tuning, and a local dashboard. Verified by a 15-check PRD proof suite on the live model.
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass
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import numpy as np
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@dataclass(frozen=True)
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class VisualToken:
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token_id: str
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vector: np.ndarray
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attention_importance: float
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evidence_value: float
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def select_dvkv_tokens(
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tokens: list[VisualToken],
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budget: int,
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diversity_weight: float = 0.35,
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evidence_weight: float = 0.30,
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) -> list[VisualToken]:
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if budget <= 0:
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raise ValueError("DVKV budget must be positive")
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if len(tokens) <= budget:
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return tokens
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selected: list[VisualToken] = []
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remaining = tokens.copy()
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first = max(remaining, key=lambda token: token.attention_importance + evidence_weight * token.evidence_value)
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selected.append(first)
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remaining.remove(first)
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while remaining and len(selected) < budget:
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scores = []
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selected_vectors = np.stack([normalize(token.vector) for token in selected])
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for token in remaining:
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vector = normalize(token.vector)
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nearest_similarity = float(np.max(selected_vectors @ vector))
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diversity = 1.0 - nearest_similarity
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score = (
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token.attention_importance
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+ diversity_weight * diversity
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+ evidence_weight * token.evidence_value
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)
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scores.append(score)
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best_index = int(np.argmax(scores))
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selected.append(remaining.pop(best_index))
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return selected
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def normalize(vector: np.ndarray) -> np.ndarray:
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norm = float(np.linalg.norm(vector))
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if norm == 0.0:
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return vector
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return vector / norm
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