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Added from claude/ruvector-lfm2-llm-01YS5Tc7i64PyYCLecT9L1dN branch: - examples/ruvLLM: Complete LLM inference system with SIMD optimization - Pretraining, benchmarking, and optimization system - Real SIMD-optimized CPU inference engine - Comprehensive SOTA benchmark suite - Attention mechanisms, memory management, router Enhanced postgres-cli with full ruvector-postgres integration: - Sparse vector operations (BM25, top-k, prune, conversions) - Hyperbolic geometry (Poincare, Lorentz, Mobius operations) - Agent routing (Tiny Dancer system) - Vector quantization (binary, scalar, product) - Enhanced graph and learning commands 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
46 lines
1.7 KiB
TOML
46 lines
1.7 KiB
TOML
# RuvLLM Example Configuration
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# Copy this file to ruvllm.toml and customize
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[system]
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device_class = "server" # edge, mobile, server, gpu
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max_memory_mb = 8192
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max_concurrent_requests = 10
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data_dir = "./data"
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[embedding]
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dimension = 768 # Embedding vector size
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max_tokens = 512 # Max tokens per input
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batch_size = 8 # Batch size for embedding
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[memory]
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db_path = "./data/memory.db"
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hnsw_m = 16 # Connections per node
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hnsw_ef_construction = 100 # Build quality
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hnsw_ef_search = 64 # Search quality
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max_nodes = 1000000 # Max memory nodes
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writeback_batch_size = 100 # Batch size for writes
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writeback_interval_ms = 1000 # Write interval
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[router]
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input_dim = 128 # Input feature dimension
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hidden_dim = 64 # Hidden state size
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sparsity = 0.9 # Weight matrix sparsity
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rank = 8 # Low-rank decomposition rank
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confidence_threshold = 0.7 # Fallback threshold
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[inference]
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models = ["tiny", "small", "medium", "large"]
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quantization = "q4" # Quantization type
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max_context = 8192 # Max context length
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max_loaded_models = 2 # Max concurrent models
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kv_cache_size = 1024 # KV cache entries
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[learning]
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enabled = true # Enable self-learning
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quality_threshold = 0.7 # Min quality for writeback
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replay_capacity = 10000 # Replay buffer size
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batch_size = 32 # Training batch size
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learning_rate = 0.001 # Learning rate
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ewc_lambda = 0.4 # EWC regularization
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training_interval_ms = 3600000 # Training interval (1 hour)
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min_samples = 100 # Min samples before training
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