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

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:

~/.claude/skills/llama-cpp/scripts/ollama_model_path.sh qwen2.5:7b

Run Inference

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):

~/.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:

curl http://localhost:8081/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"default","messages":[{"role":"user","content":"Hello"}]}'

Serve Qwen3.5

Dedicated servers for Qwen3.5 models with asymmetric KV cache, jinja templates, and thinking mode.

9B Dense (recommended for 24-36GB systems):

# Default: Qwen3.5-9B, thinking mode, 32K context
~/.claude/skills/llama-cpp/scripts/llama_serve_qwen35_9b.sh

# Full precision F16 (~17.9 GB, zero quantization loss)
~/.claude/skills/llama-cpp/scripts/llama_serve_qwen35_9b.sh ~/models/Qwen3.5-9B-BF16.gguf

# Non-thinking mode, shorter context
THINK=0 CTX=8192 ~/.claude/skills/llama-cpp/scripts/llama_serve_qwen35_9b.sh

35B MoE (for 64+ GB systems):

~/.claude/skills/llama-cpp/scripts/llama_serve_qwen35.sh  # defaults to qwen3.5:35b-a3b

9B Q4 uses ~6.6 GB (ample headroom); F16 uses ~17.9 GB (fits with 32K context on 36GB). Asymmetric KV cache (q8_0 keys + q4_0 values) saves ~60% KV memory vs FP16 cache.

F16 (Full Precision) Mode

For maximum quality (zero quantization loss), download and serve the BF16 GGUF:

# Download once (~17.9 GB)
huggingface-cli download unsloth/Qwen3.5-9B-GGUF "Qwen3.5-9B-BF16.gguf" --local-dir ~/models

# Serve F16
~/.claude/skills/llama-cpp/scripts/llama_serve_qwen35_9b.sh ~/models/Qwen3.5-9B-BF16.gguf

F16 vs Q4 on M4 Max 36GB:

	Q4_K_M (default)	BF16 (F16)
Size	6.6 GB	17.9 GB
Speed	~38 tok/s	~8-12 tok/s
Quality	~99.5%	100% (reference)
Max context	262K	~32K comfortable

Benchmark (llama.cpp vs Ollama)

~/.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):

~/.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:

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 8180+)

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