tiny-turboquant 0.4.0

Low-bit vector and KV-cache compression research toolkit for PyTorch

Tiny TurboQuant

Tiny TurboQuant is a lightweight PyTorch research toolkit for low-bit vector compression, compressed RAG retrieval, and KV-cache compression experiments.

Version 0.4.0 focuses on the RAG/vector-index roadmap:

• RAGCompressedIndex for compressed retrieval experiments
• document chunking helpers
• JSONL / folder text ingestion utilities
• compressed vector index save/load
• RAG index save/load
• retrieval metrics: recall@k, precision@k, MRR, nDCG, overlap, label-match ratio
• CLI entry point: tiny-tq rag-bench
• existing hybrid KV-cache and paged-attention research utilities from v0.3.x

Important limitation

This package demonstrates packed memory compression and memory-quality benchmarking. It is not a production compressed-attention engine. Hugging Face generation still receives dense K/V tensors. The paged attention utility dequantizes page-by-page and avoids one full dense cache tensor, but it is not a fused CUDA/Triton kernel. Real latency gains require fused kernels or serving-engine integration.

Do not use this package to claim training acceleration, fine-tuning memory reduction, production legal/medical QA readiness, drop-in vLLM replacement, exact nearest-neighbor search, or faster LLM inference.

Install

pip install tiny-turboquant

Optional demo dependencies:

pip install "tiny-turboquant[demos]"

Compressed RAG index from precomputed embeddings

import torch
from tiny_turboquant import RAGCompressedIndex

texts = [
    "Embedding compression can reduce vector-store memory in RAG systems.",
    "KV cache stores Key and Value tensors during LLM generation.",
]
embeddings = torch.randn(len(texts), 384)

index = RAGCompressedIndex.from_embeddings(
    texts,
    embeddings,
    bits=4,
    store_original_for_rerank=True,
)

results = index.search(embeddings[0], top_k=1, rerank_top_k=2)
print(index.memory_report())
print(results[0].text)

Compressed RAG index from documents

Requires sentence-transformers:

from tiny_turboquant import RAGCompressedIndex

docs = [
    "RAG systems retrieve relevant document chunks and pass them to an LLM.",
    "Compressed vector indexes reduce embedding memory usage.",
]

index = RAGCompressedIndex.from_documents(
    docs,
    embedding_model="sentence-transformers/all-MiniLM-L6-v2",
    bits=4,
    chunk_size=500,
    overlap=50,
)

results = index.search("How do we reduce vector-store memory?", top_k=3)

Save and load

index.save("rag_index.ttq")
loaded = RAGCompressedIndex.load("rag_index.ttq")

Retrieval metrics

from tiny_turboquant import recall_at_k, mrr_at_k, ndcg_at_k

retrieved = ["doc-1", "doc-2", "doc-3"]
relevant = {"doc-2", "doc-5"}

print(recall_at_k(retrieved, relevant, k=3))
print(mrr_at_k(retrieved, relevant, k=3))
print(ndcg_at_k(retrieved, relevant, k=3))

CLI

tiny-tq version

Synthetic RAG benchmark:

tiny-tq rag-bench --synthetic --bits 4 --top-k 10 --rerank-top-k 50

JSONL benchmark:

tiny-tq rag-bench \
  --input-jsonl docs.jsonl \
  --text-field text \
  --query "How can we reduce vector-store memory?" \
  --bits 4 \
  --top-k 10 \
  --rerank-top-k 50

Hybrid KV-cache usage

from tiny_turboquant import HybridTurboQuantKVCache

cache = HybridTurboQuantKVCache(
    key_bits=6,
    value_bits=4,
    key_outlier_bits=8,
    value_outlier_bits=8,
    n_key_outliers=32,
    n_value_outliers=16,
    key_recent_window=128,
    value_recent_window=64,
    per_layer_calibration=True,
    per_head_calibration=True,
)

Compressed vector index usage

import torch
from tiny_turboquant import CompressedVectorIndex

emb = torch.randn(10_000, 384)
index = CompressedVectorIndex(bits=4, store_original_for_rerank=True).add(emb)
results = index.search(emb[0], top_k=5, rerank_top_k=100)

print(index.compression_ratio())
print(results[0])

Project position

Current focus:

• memory compression
• retrieval quality measurement
• RAG/vector-index experiments
• KV-cache compression research

Future direction:

• real workload RAG benchmarks
• FAISS/vector database integration
• long-context real-model KV-cache benchmarks
• fused dequant + attention kernels
• serving-engine integration experiments