---

name: llama-cpp-backend description: "Operate the torch-free and low-memory MOSS-TTS-Delay llama.cpp backend." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

llama-cpp-backend

Use this sub-skill when the task is to run, configure, validate, convert weights for, or debug the MOSS-TTS-Delay llama.cpp bridge backend. This backend replaces the full Torch/Hugging Face generation stack with a GGUF Qwen3 backbone, NumPy embeddings/LM heads, delay-pattern sampling, and ONNX Runtime or TensorRT audio tokenizer decoding.

Scope

This sub-skill owns:

• Torch-free and torch-optional install profiles for the llama.cpp backend.
• Required GGUF, embedding, LM-head, tokenizer, ONNX, and TensorRT-engine file layout.
• The PipelineConfig YAML contract and safe config inspection before model loading.
• C bridge build requirements for libbackbone_bridge.so.
• CLI and Python API generation paths for text-to-speech and reference-audio voice cloning.
• ONNX, TensorRT, and Torch audio-tokenizer backend selection.
• heads_backend selection for NumPy versus optional Torch LM heads.
• Low-memory and 8 GB GPU tradeoffs.
• Weight extraction/conversion from a full MOSS-TTS-Delay checkpoint into backbone, embeddings, LM heads, and GGUF artifacts.
• Batch-evaluation layout and result interpretation for Seed-TTS/CV3-style suites.

Route elsewhere

• For full Torch/Hugging Face MOSS-TTS family generation, use ../hf-family-workflows/SKILL.md.
• For training, fine-tuning, or dataset preparation, use ../finetuning-data-prep/SKILL.md.
• For realtime voice-agent workflows, use ../realtime-voice-agent/SKILL.md.

Fast operating path

1. Choose an install profile and backend mix from references/build-and-configure.md.

2. Place weights in the expected layout or adapt a YAML config to your chosen paths.

3. Build the C bridge so libbackbone_bridge.so is discoverable by the installed moss_tts_delay.llama_cpp package.

4. Inspect the YAML without loading models:

   python <this-skill>/scripts/inspect_llama_cpp_config.py <config.yaml>
   python <this-skill>/scripts/inspect_llama_cpp_config.py <config.yaml> --json
   

5. Run one CLI smoke generation with a short sentence and --profile only after the config inspector reports the required path fields are present.

6. For production runs, pin audio_backend, heads_backend, n_gpu_layers, low_memory, KV-cache type, and sampling values explicitly in the YAML or CLI overrides.

Reference map

• references/build-and-configure.md — installs, weight layout, C bridge build, YAML profiles, and low-memory settings.
• references/api-and-cli.md — CLI flags, Python API, config field meanings, backend behavior, and generation caveats.
• references/weight-conversion.md — converting a full MOSS-TTS-Delay checkpoint into llama.cpp-ready artifacts.
• references/evaluation.md — batch-eval input/output layout, tasks, summaries, and quantization-quality context.
• references/troubleshooting.md — missing bridge/weights, ONNX/TRT/Torch backend failures, CUDA limits, downloads, and evaluation-layout failures.
• scripts/inspect_llama_cpp_config.py — safe YAML/path checker that does not import model code or download weights.