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Add TurboQuant compatibility, v2.1.0 ecosystem tags

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  1. README.md +56 -0
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@@ -12,6 +12,17 @@ tags:
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  - edge-device
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  - embedded
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  - iot
 
 
 
 
 
 
 
 
 
 
 
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  pipeline_tag: text-generation
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  ---
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@@ -90,3 +101,48 @@ model = hf_hub_download("ruv/ruvltra-small", "ruvltra-0.5b-q4_k_m.gguf")
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  ---
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  **License**: Apache 2.0 | **GitHub**: [ruvnet/ruvector](https://github.com/ruvnet/ruvector)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - edge-device
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  - embedded
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  - iot
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+ - turboquant
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+ - kv-cache-compression
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+ - flash-attention
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+ - speculative-decoding
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+ - graph-rag
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+ - hybrid-search
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+ - vector-database
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+ - ruvector
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+ - diskann
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+ - mamba-ssm
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+ - colbert
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  pipeline_tag: text-generation
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  ---
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  ---
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  **License**: Apache 2.0 | **GitHub**: [ruvnet/ruvector](https://github.com/ruvnet/ruvector)
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+
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+
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+ ---
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+
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+ ## âš¡ TurboQuant KV-Cache Compression
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+ RuvLTRA models are fully compatible with **TurboQuant** — 2-4 bit KV-cache quantization that reduces inference memory by 6-8x with <0.5% quality loss.
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+
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+ | Quantization | Compression | Quality Loss | Best For |
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+ |-------------|-------------|--------------|----------|
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+ | 3-bit | 10.7x | <1% | **Recommended** — best balance |
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+ | 4-bit | 8x | <0.5% | High quality, long context |
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+ | 2-bit | 32x | ~2% | Edge devices, max savings |
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+
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+ ### Usage with RuvLLM
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+
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+ ```bash
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+ cargo add ruvllm # Rust
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+ npm install @ruvector/ruvllm # Node.js
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+ ```
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+
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+ ```rust
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+ use ruvllm::quantize::turbo_quant::{TurboQuantCompressor, TurboQuantConfig, TurboQuantBits};
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+
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+ let config = TurboQuantConfig {
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+ bits: TurboQuantBits::Bit3_5, // 10.7x compression
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+ use_qjl: true,
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+ ..Default::default()
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+ };
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+ let compressor = TurboQuantCompressor::new(config)?;
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+ let compressed = compressor.compress_batch(&kv_vectors)?;
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+ let scores = compressor.inner_product_batch_optimized(&query, &compressed)?;
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+ ```
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+
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+ ### v2.1.0 Ecosystem
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+
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+ - **Hybrid Search** — Sparse + dense vectors with RRF fusion (20-49% better retrieval)
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+ - **Graph RAG** — Knowledge graph + community detection for multi-hop queries
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+ - **DiskANN** — Billion-scale SSD-backed ANN with <10ms latency
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+ - **FlashAttention-3** — IO-aware tiled attention, O(N) memory
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+ - **MLA** — Multi-Head Latent Attention (~93% KV-cache compression)
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+ - **Mamba SSM** — Linear-time selective state space models
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+ - **Speculative Decoding** — 2-3x generation speedup
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+
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+ [RuVector GitHub](https://github.com/ruvnet/ruvector) | [ruvllm crate](https://crates.io/crates/ruvllm) | [@ruvector/ruvllm npm](https://www.npmjs.com/package/@ruvector/ruvllm)