🚀 RuvLLM v2.3 - RuvLTRA-Medium 3B + Task-Specific LoRA + HuggingFace Hub

[ruvnet]

🚀 RuvLLM v2.3 - High-Performance LLM Inference for Apple Silicon

Run Large Language Models locally on your Mac with maximum performance

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🎯 What's New in v2.3

🧠 RuvLTRA-Medium 3B Model

Purpose-built 3B model optimized for Claude Flow agent orchestration:

Spec	Value
Parameters	3.0B
Hidden Size	2560
Layers	42
Context	256K tokens
Features	Flash Attention 2, Speculative Decoding, SONA Hooks

🔌 HuggingFace Hub Integration

Full Hub integration for model distribution:

use ruvllm::hub::{ModelDownloader, ModelUploader, RuvLtraRegistry};

// Download from Hub
let downloader = ModelDownloader::new(DownloadConfig::default());
let path = downloader.download("ruvector/ruvltra-small-q4km", None)?;

// Upload to Hub
let uploader = ModelUploader::new("hf_token");
uploader.upload("./model.gguf", "username/my-model", metadata)?;

🎯 Task-Specific LoRA Adapters

5 pre-trained adapters optimized for Claude Flow agent types:

Adapter	Rank	Alpha	Targets	Use Case
Coder	16	32.0	Q,K,V,O	Code generation, refactoring
Researcher	8	16.0	Q,K,V	Information analysis
Security	16	32.0	Attention + MLP	Vulnerability detection
Architect	12	24.0	Q,V + Gate,Up	System design
Reviewer	8	16.0	Q,V	Code review

🔄 Adapter Merging & Hot-Swap

Advanced adapter composition strategies:

Strategy	Description
TIES	Trim, Elect, Merge for robust composition
DARE	Drop And REscale for sparse merging
SLERP	Spherical interpolation for smooth transitions
TaskArithmetic	Add/subtract task vectors

// Hot-swap adapters at runtime
let mut manager = HotSwapManager::new();
manager.set_active(coder_adapter);
manager.prepare_standby(security_adapter);
manager.swap()?; // Zero-downtime switch

📊 Claude Dataset Training

2,700+ training examples for Claude Flow optimization:

• Code generation (900 examples)
• Research & analysis (450 examples)
• Security review (450 examples)
• Architecture design (450 examples)
• Code review (450 examples)

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📈 v2.0-2.2 Features

🧠 Apple Neural Engine (ANE) Backend - 261-989x Faster Matmul

Native Core ML integration with Apple's Neural Engine:

Component	Technology	Benefit
Matrix Multiply	Core ML → ANE	261-989x faster vs NEON
Attention	Metal GPU	Optimized for M4 Pro
Activations	ARM NEON SIMD	2.2x faster than ANE
Auto-Dispatch	Hybrid Pipeline	Best of all worlds

🔄 SONA Self-Learning System

Three-tier learning loops for continuous optimization:

Instant Loop   → <1ms per request (MicroLoRA)
Background Loop → ~10s hourly (BaseLoRA + EWC++)
Deep Loop      → ~10min weekly (Pattern consolidation)

🤖 RuvLTRA-Small: Qwen 0.5B Optimized for Claude Flow

Spec	Value
Base Model	Qwen2.5-0.5B-Instruct
Parameters	494M
Hidden Size	896
Layers	24
Context	32K tokens

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🏎️ Performance Benchmarks (M4 Pro)

Inference Speed

Model	Quant	Prefill	Decode	Memory
RuvLTRA-Small	Q4K	3,500	135	491 MB
RuvLTRA-Medium	Q4K	2,200	85	1.8 GB
Qwen2.5-7B	Q4K	2,800	95	4.2 GB
Llama3-8B	Q4K	2,600	88	4.8 GB

Kernel Performance

Kernel	Single-thread	Multi-thread (10-core)
GEMM 4096×4096	1.2 GFLOPS	12.7 GFLOPS
Flash Attention (2048)	850μs	320μs
HNSW Search (k=10)	24.0μs	-
SONA Adapt	<1ms	-

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📦 Installation

Rust

[dependencies]
ruvllm = { version = "2.3", features = ["inference-metal", "coreml", "parallel"] }

npm

npm install @ruvector/ruvllm

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🔗 Links

• Crate README
• npm Package
• API Docs
• Adapter Documentation

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✅ Implementation Status

• RuvLTRA-Small (0.5B) model
• RuvLTRA-Medium (3B) model
• Apple Neural Engine backend
• SONA self-learning system
• Flash Attention 2
• Paged KV Cache
• Speculative Decoding
• HuggingFace Hub integration
• Task-specific LoRA adapters
• Adapter merging (TIES, DARE, SLERP)
• Hot-swap adapter management
• Claude dataset training system
• HNSW semantic routing (150x faster)