llama-cpp: Скилл локального LLM инференса

--- name: llama-cpp description: Secondary local LLM inference engine via llama.cpp. This skill should be used when running GGUF models directly, loading LoRA adapters for Kothar, benchmarking inference speed, or serving models via llama-server. Complements Ollama (which remains primary for RLAMA and general use). user-invocable: false --- # llama.cpp - Secondary Inference Engine Direct access to llama.cpp for faster inference, LoRA adapter loading, and benchmarking on Apple Silicon. Ollama remains primary for RLAMA and general use; llama.cpp is the power tool. ## Prerequisites ```bash brew install llama.cpp ``` Binaries: `llama-cli`, `llama-server`, `llama-embedding`, `llama-quantize` ## Quick Reference ### Resolve Ollama Model to GGUF Path To avoid duplicating model files, resolve an Ollama model name to its GGUF blob path: ```bash ~/.claude/skills/llama-cpp/scripts/ollama_model_path.sh qwen2.5:7b ``` ### Run Inference ```bash GGUF=$(~/.claude/skills/llama-cpp/scripts/ollama_model_path.sh qwen2.5:7b) llama-cli -m "$GGUF" -p "Your prompt here" -n 128 --n-gpu-layers all --single-turn --simple-io --no-display-prompt ``` ### Start API Server To start an OpenAI-compatible server (port 8081, avoids Ollama's 11434): ```bash ~/.claude/skills/llama-cpp/scripts/llama_serve.sh <model.gguf> # Or with options: PORT=8082 CTX=8192 ~/.claude/skills/llama-cpp/scripts/llama_serve.sh <model.gguf> ``` Test the server: ```bash curl http://localhost:8081/v1/chat/completions -H "Content-Type: application/json" -d '{"model":"default","messages":[{"role":"user","content":"Hello"}]}' ``` ### Serve Qwen3.5 (MoE-Optimized) Dedicated server for Qwen3.5 models with asymmetric KV cache, jinja templates, and thinking mode: ```bash # Default: Qwen3.5-35B-A3B, thinking mode, 16K context ~/.claude/skills/llama-cpp/scripts/llama_serve_qwen35.sh # Non-thinking mode, shorter context THINK=0 CTX=8192 ~/.claude/skills/llama-cpp/scripts/llama_serve_qwen35.sh # Different model ~/.claude/skills/llama-cpp/scripts/llama_serve_qwen35.sh qwen3.5:27b ``` Memory-optimized for M4 Max 36GB: asymmetric KV cache (q8_0 keys + q4_0 values) saves ~60% KV memory vs FP16. ### Benchmark (llama.cpp vs Ollama) ```bash ~/.claude/skills/llama-cpp/scripts/llama_bench.sh qwen2.5:7b ``` Reports prompt processing and generation tok/s for both engines side by side. ### LoRA Adapter Inference Load a LoRA adapter dynamically on top of a base GGUF model (no merge required): ```bash ~/.claude/skills/llama-cpp/scripts/llama_lora.sh <base.gguf> <lora.gguf> "Your prompt" ``` This is the key advantage over Ollama: hot-swap LoRA adapters without rebuilding models. ### Convert Kothar LoRA to GGUF Convert HuggingFace LoRA adapters from the Kothar training pipeline into a merged GGUF model: ```bash python3 ~/.claude/skills/llama-cpp/scripts/convert_lora_to_gguf.py --base NousResearch/Hermes-2-Mistral-7B-DPO --lora <path-or-hf-id> --output kothar-q4_k_m.gguf --quantize q4_k_m ``` ## When to Use llama.cpp vs Ollama | Task | Use | |------|-----| | RLAMA queries | Ollama (native integration) | | Quick model chat | Ollama (`ollama run`) | | LoRA adapter testing | llama.cpp (`llama_lora.sh`) | | Benchmarking tok/s | llama.cpp (`llama_bench.sh`) | | Maximum inference speed | llama.cpp (10-20% faster) | | Custom server config | llama.cpp (`llama_serve.sh`) | | Embedding generation | Either (Ollama simpler, llama-embedding more control) | | Kothar GGUF conversion | llama.cpp (`convert_lora_to_gguf.py`) | ## Architecture ``` Ollama (primary, port 11434) llama.cpp (secondary, port 8081) ├── RLAMA RAG queries ├── LoRA adapter hot-loading ├── Model management (pull/list) ├── Benchmarking ├── General chat ├── Custom server configs └── Embeddings (nomic-embed-text) └── Kothar GGUF conversion Both share the same GGUF model files (~/.ollama/models/blobs/) ``` ## Subprocess Best Practices (Build 7940+) When calling llama-cli from scripts or subprocesses: - **Always use `--single-turn`** — generates one response then exits (prevents interactive chat mode hang) - **Always use `--simple-io`** — suppresses ANSI spinner that floods redirected output - **Always use `--no-display-prompt`** — suppresses prompt echo - **Use `--n-gpu-layers all`** instead of legacy `-ngl 999` - **Use `--flash-attn on`** (not bare `--flash-attn`) — now takes argument - **Timing stats** appear in stdout as `[ Prompt: X t/s | Generation: Y t/s ]` (via `--show-timings`, default: on) - **Redirect stderr to file, not variable** — spinner output can overflow bash variables