Vector Quantization, in Pytorch. Contribute to IPC-USTB/vector-quantize-pytorch development by creating an account on GitHub.
Vector (and Scalar) Quantization, in Pytorch. Contribute to lucidrains/vector-quantize-pytorch development by creating an account on GitHub.
* Unofficial: <GitHub - thuanz123/enhancing-transformers> TL;DR 改进版的VQGAN,受到NLP任务重autoregressive pretraining的启发,利用VIT进行离散化编码和解码,同时也优化了codebook的学习,极大地提升了vector-quantized Image modeling的效果 Method 方法主要分为两个阶段,如Figure 1所示。第一阶段是Image Quantization...
1.stage1(image quantization ViT-VQGAN): 基于ViT的VQGAN encoder。基于VQGAN做了从架构到码本学习方式的多种改进——>提升了efficiency和reconstruction fidelity. 包括logits-laplace loss,L2 loss,adversarial loss 和 perceptual loss. 2.stage2(vector-quantized image modeling VIM): 学习了一个自回归的transfor...
{`vector.dimensions`: 1536, `vector.hnsw.m`: 16, `vector.quantization.enabled`: TRUE, `vector.similarity_function`: "COSINE", `vector.hnsw.ef_construction`: 100}, indexProvider: "vector-2.0"}, indexProvider: "vector-2.0"} | "" | "CREATE VECTOR INDEX `moviePlots` FOR (n:`Movie`) ...
We recommend setting the former to a multiple of 32 while the latter is limited to a range of 4-8 bits. By default, RAFT selects a dimensionality value that minimizes quantization loss according topq_bits, but this value can be adjusted to lower the memory footprint for each vector. It...
Configure quantization and storage (preview) Configure a vectorizer for queries (preview) Chunk documents Generate embeddings Integrated vectorization with Azure AI Studio models Keyword search Hybrid search Security Development Monitoring and performance Knowledge store Responsible AI Reference Resources Prenos ...
Service management Index management Vector search Create a vector index Index binary data for vector search Query vectors Filter vectors Vector quotas and staying under limits Configure quantization and storage (preview) Configure a vectorizer for queries (preview) ...
Binary QuantizationAdded in 0.7.0Use expression indexing for binary quantizationCREATE INDEX ON items USING hnsw ((binary_quantize(embedding)::bit(3)) bit_hamming_ops); Get the nearest neighbors by Hamming distanceSELECT * FROM items ORDER BY binary_quantize(embedding)::bit(3) <~> binary_...
to be twice as small as with thepq_bits = 8. Though much stronger pq_bits affects the size of the LUT, as it is proportional to2pq_bits. This has a drastic effect on recall. For more details about thelut_sizeformula, see theProduct quantizationsection in the first part of this post...