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hermes-agent/skills/mlops/llama-cpp/references/optimization.md
teknium f172f7d4aa Add skills tools and enhance model integration
- Introduced new skills tools: `skills_categories`, `skills_list`, and `skill_view` in `model_tools.py`, allowing for better organization and access to skill-related functionalities.
- Updated `toolsets.py` to include a new `skills` toolset, providing a dedicated space for skill tools.
- Enhanced `batch_runner.py` to recognize and validate skills tools during batch processing.
- Added comprehensive tool definitions for skills tools, ensuring compatibility with OpenAI's expected format.
- Created new shell script `test_skills_kimi.sh` for testing skills tool functionality with Kimi K2.5.
- Added example skill files demonstrating the structure and usage of skills within the Hermes-Agent framework, including `SKILL.md` for example and audiocraft skills.
- Improved documentation for skills tools and their integration into the existing tool framework, ensuring clarity for future development and usage.
2026-01-30 07:39:55 +00:00

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Performance Optimization Guide

Maximize llama.cpp inference speed and efficiency.

CPU Optimization

Thread tuning

# Set threads (default: physical cores)
./llama-cli -m model.gguf -t 8

# For AMD Ryzen 9 7950X (16 cores, 32 threads)
-t 16  # Best: physical cores

# Avoid hyperthreading (slower for matrix ops)

BLAS acceleration

# OpenBLAS (faster matrix ops)
make LLAMA_OPENBLAS=1

# BLAS gives 2-3× speedup

GPU Offloading

Layer offloading

# Offload 35 layers to GPU (hybrid mode)
./llama-cli -m model.gguf -ngl 35

# Offload all layers
./llama-cli -m model.gguf -ngl 999

# Find optimal value:
# Start with -ngl 999
# If OOM, reduce by 5 until fits

Memory usage

# Check VRAM usage
nvidia-smi dmon

# Reduce context if needed
./llama-cli -m model.gguf -c 2048  # 2K context instead of 4K

Batch Processing

# Increase batch size for throughput
./llama-cli -m model.gguf -b 512  # Default: 512

# Physical batch (GPU)
--ubatch 128  # Process 128 tokens at once

Context Management

# Default context (512 tokens)
-c 512

# Longer context (slower, more memory)
-c 4096

# Very long context (if model supports)
-c 32768

Benchmarks

CPU Performance (Llama 2-7B Q4_K_M)

Setup Speed Notes
Apple M3 Max 50 tok/s Metal acceleration
AMD 7950X (16c) 35 tok/s OpenBLAS
Intel i9-13900K 30 tok/s AVX2

GPU Offloading (RTX 4090)

Layers GPU Speed VRAM
0 (CPU only) 30 tok/s 0 GB
20 (hybrid) 80 tok/s 8 GB
35 (all) 120 tok/s 12 GB