pi-mono/packages/coding-agent/docs/llama-cpp.md

Local Models with llama.cpp

Pi supports the llama.cpp router server. The router discovers multiple GGUF models and loads or unloads them on demand.

Use a current llama.cpp build with router support. Follow the build instructions or install a prebuilt release for your platform.

Pi can reach llama.cpp in two ways:

• Managed: Pi starts llama-server when a llama.cpp model is first used and stops it when no Pi process uses it anymore. Use this unless you already run a server.
• Connect: Pi connects to a router you start and manage yourself, for example on another machine or shared with other clients.

Managed server

Install llama.cpp so that llama-server is on your PATH, then run:

/login llama.cpp

Select Start llama-server automatically. Pi checks that llama-server --version runs and stores the choice. It does not start the server yet.

The server starts the first time a llama.cpp model is used or /llama is opened. Pi binds it to 127.0.0.1 on a random free port and protects it with a random API key, so it never conflicts with other servers and is not reachable from other machines. All Pi processes share one server. After the last Pi process exits, the server keeps running for llamaCpp.idleShutdownSeconds (30 seconds by default) and then stops. A Pi process started in that window reuses the running server and its loaded models.

Models come from ~/.pi/agent/llama/models and from the llama.cpp download cache. Put GGUF files in the models directory using the layout described in Connect to a router, or download models with /llama. Downloads go to the llama.cpp Hugging Face cache (~/.cache/huggingface/hub by default, or LLAMA_CACHE, HF_HUB_CACHE, or HF_HOME when set). /llama shows both directories.

All models in the managed server appear in /model and load when selected, unless llamaCpp.args contains --no-models-autoload. The request waits while the model loads. Pi reads the model list only from a running server and remembers it, so starting Pi does not start the server. Right after /login, open /llama once to populate /model.

Settings

Configure the managed server in ~/.pi/agent/settings.json. Project settings cannot change it because it selects a command to run.

{
  "llamaCpp": {
    "command": "llama-server",
    "modelsDir": "~/models",
    "args": ["--jinja", "-ngl", "999", "-c", "32768"],
    "idleShutdownSeconds": 30
  }
}

Setting	Default	Description
command	llama-server	Executable to run. Supports a leading ~.
modelsDir	~/.pi/agent/llama/models	Directory with local GGUF files. Supports a leading ~.
args	[]	Extra llama-server arguments, such as -c or --models-preset. --host, --port, --api-key, --models-dir, and single-model options (-m, -hf, ...) are rejected because Pi controls them.
idleShutdownSeconds	30	How long the server keeps running after the last Pi process exits. 0 stops it right away.

Settings apply when the server starts. After changing them, choose Restart server in /llama. Restarting unloads all models and interrupts requests from other Pi processes.

The server inherits the environment of the Pi process that started it. If HF_TOKEN is not set, Pi passes the Hugging Face token it finds (see Manage models) so gated downloads work.

Logs

The server writes to ~/.pi/agent/llama/server.log, which starts fresh each time a new server starts. Choose View log in /llama to see the last lines. If the server fails to start, the error in Pi names the log file.

Connect to a router

Start llama-server without --model or -m. Passing a model starts single-model mode instead of router mode.

llama-server \
  --models-dir ~/models \
  --no-models-autoload \
  --jinja \
  --host 127.0.0.1 \
  --port 8080 \
  -ngl 999 \
  -c 32768

Important options:

• --models-dir ~/models discovers local GGUF files.
• --no-models-autoload keeps loading explicit through /llama.
• --jinja enables compatible chat templates and tool calling.
• -ngl 999 offloads as many layers as possible to the GPU.
• -c 32768 sets the context window for each loaded model. Omit it to use the model's native context, which may require substantially more memory.

A single-file model can sit directly in the model directory. Put multimodal and multi-shard models in separate subdirectories:

~/models/
├── llama-3.2-1b-Q4_K_M.gguf
├── gemma-3-4b-it-Q4_K_M/
│   ├── gemma-3-4b-it-Q4_K_M.gguf
│   └── mmproj-F16.gguf
└── large-model-Q4_K_M/
    ├── large-model-Q4_K_M-00001-of-00003.gguf
    ├── large-model-Q4_K_M-00002-of-00003.gguf
    └── large-model-Q4_K_M-00003-of-00003.gguf

Restart the router after manually adding files. For per-model context sizes and other options, use llama.cpp model presets.

Start Pi and configure the provider:

/login llama.cpp

Select Connect to a running server, then enter the router URL and optional API key. The default URL is http://127.0.0.1:8080.

If you start the router with --no-models-autoload, /login llama.cpp only stores the connection. Run /llama to load a model, then /model to select the loaded model for the current session.

Environment variables can configure the same values without /login:

export LLAMA_BASE_URL=http://127.0.0.1:8080
export LLAMA_API_KEY=optional-secret
pi

If the server uses an API key, start llama-server with the matching --api-key value. Keep --host 127.0.0.1 for local-only access.

Manage models

Run:

/llama

• Select an unloaded model to load it.
• Select a loaded model to unload it.
• Select Download model…, search Hugging Face, then choose a repository and quantization. Exact owner/repository[:quant] values also work.
• Press Escape during a load or download to confirm cancellation.

With a managed server, /llama also shows the models and download directories and offers Restart server and View log.

Hugging Face search uses HF_TOKEN when set, then checks $HF_TOKEN_PATH, $HF_HOME/token, $XDG_CACHE_HOME/huggingface/token, and ~/.cache/huggingface/token. Search also works without authentication, subject to lower rate limits. Pi warns before downloading gated repositories and links to their access page. The llama.cpp server performs the download, so its process must also have HF_TOKEN when the selected repository requires access.

If other models are loaded, Pi asks whether to unload them first or keep them loaded. Pi does not silently unload models and never deletes model files. The router may be shared with other clients, so /llama always displays the router's current state.

Loaded and sleeping models appear in /model. Sleeping models wake automatically when selected. With router autoload enabled, unloaded preset models also appear and load when selected; with a managed server, all unloaded models do. With --no-models-autoload, load a model through /llama before selecting it.

If the router disconnects, /llama shows Retry and Close. Retry reconnects and refreshes model state without replaying the interrupted operation.

Classification

Every model listed for chat is also listed as a classifier model with the same ID and the llama-cpp-classify API. Classifier models answer typed choice, bool, and score questions about JSON state, like TypeSafe's Jev models. The model reaches them from codemode scripts, and extensions through ctx.modelRegistry.classify(); see Classifier models.

The model does not generate an answer. Each question becomes one chat prompt: the state, every question of the request, the state again, and then the question with its answers under single-token labels. Labels are letters for a choice (up to 62 options), Yes/No for a bool, and digits for a score (up to 10 levels). The second copy of the state is read with the questions in view, which improved accuracy on JevBench with small models. Pi reads the probabilities of the labels as the next token and normalizes them. A choice returns every option's probability and a confidence of (n * peak - 1) / (n - 1); a score returns the expected level.

• Raw label probabilities are usually overconfident. The per-request temperature option divides the label logits before normalizing; values above 1 soften the distribution. It changes no answer.
• Questions run one after another. Everything before the final question is the same for all questions of a request, so the server's prompt cache evaluates it once. The state appears twice, so it needs twice its size in context.
• Small models may follow instructions written inside the state. The prompt tells the model to judge the state as data, but that is not a guarantee.
• Hybrid models such as Qwen3.5 cannot rewind a partially cached prompt without context checkpoints. If each question reprocesses the whole state, start the router with --ctx-checkpoints 32 --checkpoint-min-step 0.

Troubleshooting

For a managed server:

• /login cannot find llama-server: Install llama.cpp or set llamaCpp.command to the full path.
• The server fails to start: Read ~/.pi/agent/llama/server.log. Invalid llamaCpp.args usually show up there.
• Models missing after adding files: Choose Restart server in /llama.
• Server is still running: It stops llamaCpp.idleShutdownSeconds after the last Pi process exits. If a supervisor process was killed with SIGKILL, llama-server may keep running; stop it manually.

For a router you started, check that it is reachable:

curl http://127.0.0.1:8080/health
curl http://127.0.0.1:8080/models

• No models in /llama: Check --models-dir, the directory layout, and restart the router.
• Model missing from /model with --no-models-autoload: Load it with /llama first.
• Load fails or uses too much memory: Lower -c or unload another model.
• Server is not in router mode: Start it without --model, -m, or -hf.

To remove the llama.cpp provider and /llama, disable llama.cpp under Built-in in pi config, or set "extensions": ["-builtin:llama.cpp"] in settings.