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List and toggle local LLMs (GGUF via llama.cpp, vLLM via Docker) from per-model YAML configs

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ZIP 다운로드

- llama-gguf-vision.yaml
- llama-gguf.yaml
- vllm-dual.yaml
- vllm.yaml
- - __init__.py
  - cluster.py
  - loader.py
- - __init__.py
  - base.py
  - dual_serve.py
  - llama.py
  - llama_dual.py
  - sglang.py
  - sglang_dual.py
  - systemd.py
  - vllm.py
  - vllm_dual.py
  - vllm_dual_ray.py
  - wait.py
- - __init__.py
  - checks.py
  - io.py
  - memory.py
  - usage.py
- __init__.py
- __main__.py
- cli.py
- sync.py
- - __init__.py
  - minimal_tree.py
- __init__.py
- conftest.py
- test_checks.py
- test_cli.py
- test_cluster.py
- test_cmd_serve.py
- test_dual_serve.py
- test_glm53_exl3_recipe.py
- test_llama_cmd.py
- test_llama_dual.py
- test_process_lifecycle.py
- test_sglang.py
- test_sglang_dual.py
- test_sync.py
- test_usage.py
- test_vllm_and_abort.py
- test_vllm_dual.py
- test_vllm_dual_ray.py
- test_vllm_logging.py
.gitignore
LICENSE
pyproject.toml
README.md
SKILL.md
uv.lock

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```
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╚══════╝╚═╝     ╚═╝╚══════╝ ╚══╝╚══╝ ╚═╝   ╚═╝    ╚═════╝╚═╝  ╚═╝
```

> local LLM switcher · GGUF + vLLM

List and toggle local LLMs from per-model YAML configs.

> **Adding a model?** See [`SKILL.md`](SKILL.md) — a self-contained guide (for
> humans or AI agents) to authoring `ai-models/*.yaml` recipes: every key, the
> `extra_args` passthrough, and the GB10 gotchas. Point an agent at this folder
> and it can add a model from that doc alone.

`lmswitch` shows a table of every configured model (grouped by family) with its
size, download state, port, and whether it's currently serving — and lets you
start/stop them interactively. **GGUF** models run under
[`llama.cpp`](https://github.com/ggml-org/llama.cpp) (`llama-server` as a
background process); **vLLM** models run in Docker. It waits for each model to
actually become ready, refuses loads that would exceed free RAM, and keeps your
coding agents' configs in sync — [opencode](https://opencode.ai),
[hermes](https://github.com/NousResearch/hermes-agent), and
[grok](https://github.com/xai-org/grok-cli) — with whatever is serving.

Running `lmswitch` (the same wordmark above greets you):

```
  RAM    │ 122Gi total   33Gi used   89Gi available
  Models │ ~20.8G weights   1 / 3 loaded
  Disk   │ 113.1G   3 / 3 downloaded
  ● loaded   ○ stopped      ✓ downloaded   ✗ missing

#  S  TYPE   NAME            SIZE  DL   PORT  DISPLAY
   Qwen ──────────────────────────────────────────────────────────────────────
   1  ○   gguf   qwen3-4b        2.3G  ✓   8085  Qwen3-4B
   2  ●   gguf   qwen3.6-35b    20.8G  ✓   8089  Qwen3.6-35B-A3B
   Nex ───────────────────────────────────────────────────────────────────────
   3  ○   gguf   nex-n2-pro     90.0G  ✓   8104  Nex-N2-Pro 397B-A17B (IQ1_M)

Toggle # (space/comma separated, enter or q to quit):
```

## Features

- **One table for everything** — loaded (`●`) vs stopped (`○`), downloaded (`✓`)
  vs missing (`✗`), per-model size/port, and RAM / disk / loaded-count totals.
- **Six runtimes** — GGUF via `llama-server`, safetensors/quantized via vLLM
  in Docker, SGLang in Docker (`sglang`), and three ways to serve one model
  across **two DGX Sparks** over a CX7 link: `vllm-dual` and `sglang-dual`
  (both tensor-parallel) and `llama-dual` (GGUF split over llama.cpp RPC).
  Pick per model with `runtime:`.
- **Cluster view (optional)** — with `CLUSTER_HOSTS` set, the table merges the
  other node's models with a HOST column (`spark` / `gigabyte` / `dual`) and
  toggling a peer's model delegates over SSH. Without it, nothing changes:
  single-box output and behavior are identical to previous versions.
- **Readiness-aware** — after launch it polls the model's `/v1/models` endpoint
  and only reports `Ready` once it's actually serving (with a `…loading` progress
  heartbeat and crash detection), so the synced configs reflect reality, not guesses.
- **Pre-load RAM guard** — refuses a start that would blow past available memory
  (overridable per-model with `force: true`), so a too-big model can't OOM-lock
  the machine.
- **Config sync** — on every toggle / `on` / `off` / `sync`, the
  currently-serving models are written into your coding agents' configs:
  **opencode** (`opencode.json`), **hermes** (`config.yaml`), and **grok**
  (`config.toml`). Pick which targets are active during `lmswitch init`. See
  [Config sync](#config-sync).
- **Optional systemd auto-restart** per model via `restart: on-failure`.

## Requirements

lmswitch targets **Linux** (it uses `/proc/meminfo`, `ss`, Docker `--gpus`, and
systemd user units).

| For | You need |
|-----|----------|
| lmswitch itself | Python 3.10+, `curl`, `ss` (iproute2). `pyyaml` is installed automatically as a dependency (a minimal built-in parser is used as a fallback if it's ever missing). |
| GGUF models | A built `llama.cpp` with `llama-server` (a CUDA build for GPU offload). Default binary path: `<lmswitch>/../llama.cpp/build/bin/llama-server` — override per-model with `llama_bin:`. |
| vLLM models | Docker + the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/) (`--gpus all`). Pulls the `vllm/vllm-openai` image. |
| `restart: on-failure` | A running systemd **user** instance (`systemctl --user`). |
| config sync | Any of [opencode](https://opencode.ai), [hermes](https://github.com/NousResearch/hermes-agent), [grok](https://github.com/xai-org/grok-cli) (all optional — only configs that exist are synced). |

## Submodule

This repo includes a git submodule (`ai-models/`) with community-contributed YAML
recipes for GGUF and vLLM models. The submodule lives at
[jvr0x/ai-models](https://github.com/jvr0x/ai-models).

```bash
# Clone with recipes baked in:
git clone --recurse-submodules https://github.com/jvr0x/lmswitch.git

# If you already cloned (without --recurse):
cd lmswitch
git submodule update --init
```

If you only need the recipes (no lmswitch code), clone the submodule repo
directly: https://github.com/jvr0x/ai-models

## Install

lmswitch is a Python package exposing a `lmswitch` console-script entry point
(`lmswitch = "lmswitch.cli:main"`). The cleanest install is as an isolated CLI
tool with [uv](https://docs.astral.sh/uv/) (or pipx) — it keeps lmswitch's deps
out of your system Python and sidesteps the `externally-managed-environment`
(PEP 668) error on Debian/Ubuntu:

```bash
# from the repo dir (e.g. ~/utils/lmswitch)
uv tool install -e .        # editable; puts `lmswitch` on your PATH (~/.local/bin)
uv tool update-shell        # one-time: ensure uv's bin dir is on $PATH
lmswitch init
```

<details>
<summary>Other install methods</summary>

```bash
pipx install -e .                                  # same idea, via pipx
pip install --user -e . --break-system-packages    # plain pip --user (overrides PEP 668)
pip install -e .                                    # inside an activated virtualenv
```
</details>

`init` asks where your models live (writes `ai-models/.lmswitch`), creates the
`ai-models/` config dir, and asks which sync targets to enable (opencode / hermes
/ grok — only the ones whose configs it finds). It does **not** reinstall the
command: if a `lmswitch` console script is already on your `PATH` (from the step
above) it leaves it alone; only if none is found does it drop a small launcher in
`~/.local/bin` pinned to the current interpreter. Ensure `~/.local/bin` is on
your `$PATH`.

Upgrade after pulling changes with `uv tool upgrade lmswitch` (an editable
install picks up code edits automatically); remove with `uv tool uninstall
lmswitch`.

## Getting started

1. Install the requirements above.
2. `lmswitch init` — set your models directory (default `~/models`).
3. Download a model into that directory (see **Where to get models**).
4. Create a config — `lmswitch add <name>`, or copy a template from
   [`examples/`](examples/) into `ai-models/<name>.yaml`.
5. `lmswitch` → type the model's number to start it. It loads, waits until the
   endpoint answers, prints `Ready on port <port>`, and syncs your enabled
   configs (opencode / hermes / grok).
6. Hit it: `curl localhost:<port>/v1/models`.

## Usage

```
lmswitch                  # interactive: show the table, then type model #s to toggle
lmswitch list             # just print the table (read-only)
lmswitch on  <name|#>     # start a model
lmswitch off <name|#>     # stop a model
lmswitch sync             # regenerate enabled configs from currently-serving models
lmswitch add  <name>      # create a model config interactively
lmswitch serve <name>     # run a model in the foreground (used by systemd)
lmswitch init             # bootstrap ai-models/, .lmswitch, and sync targets
lmswitch -h, --help       # show help
lmswitch -v, --version    # print the version
```

In the interactive prompt you can toggle several at once, space/comma separated:
`8 9 24`. A toggle blocks until the model is ready (or its `ready_timeout`
elapses); `Ctrl-C` aborts cleanly — the model keeps loading detached, so re-run
`lmswitch` or `lmswitch sync` to pick it up once it's up.

## Model configs (`ai-models/<name>.yaml`)

The filename (minus `.yaml`) is the model's id, its `served-model-name`, and —
for vLLM — its container name (`vllm-<id>`). `model:` is a path **relative to
your models directory**. Fully-commented templates live in
[`examples/llama-gguf.yaml`](examples/llama-gguf.yaml) and
[`examples/vllm.yaml`](examples/vllm.yaml).

**Common keys**

| Key | Default | Meaning |
|-----|---------|---------|
| `runtime` | `llama` | `llama` (GGUF), `vllm` (Docker), `sglang` (Docker), or the two-node `vllm-dual` / `sglang-dual` / `llama-dual` |
| `model` | — | path to the `.gguf` file (llama) or model dir (vLLM), relative to the models dir |
| `port` | `8081` | OpenAI-compatible server port |
| `ctx` | `65536` | context length |
| `display_name` | `<name>` | label in the table / synced configs |
| `ready_timeout` | `600` (vLLM) / `300` (llama) | seconds to wait for readiness |
| `force` | `false` | bypass the pre-load RAM guard |
| `restart` | — | `on-failure` → run under a systemd user unit |

**llama (GGUF) keys**: `gpu_layers` (99), `threads` (12), `batch` (1024),
`ubatch` (512), `alias`, `mmproj`, `llama_bin`, and `fit` — default `off`, which
skips llama.cpp's auto memory-fit step (it aborts in `cudaMemGetInfo` on some
CUDA builds, e.g. GB10/Blackwell); set `fit: none` to omit the flag entirely on
older llama.cpp builds that don't support `-fit`.

**vLLM keys**: `gpu_memory_utilization` (0.15), `image`, `tool_call_parser`,
`reasoning_parser`, `trust_remote_code`, `max_num_seqs`, `extra_args`, and more
— see [`examples/vllm.yaml`](examples/vllm.yaml) for the full list.

**SGLang keys**: `mem_fraction_static` (0.95 — a hard up-front reservation, so
the RAM guard sizes the model from it, not from the weights), `image`,
`attention_backend`, `chunked_prefill_size`, `disable_prefill_cuda_graph`
(true), `kv_cache_dtype`, `tp_size`, `max_running_requests`,
`tool_call_parser`, `reasoning_parser`, `sampling_defaults`, `cpuset`
(`5-9,15-19` — GB10's performance cores; `""` to disable pinning), `shm_size`
(32g), `json_model_override_args`, plus the shared `extra_mounts` / `env` /
`extra_args`. Containers are named `sglang-<id>`. A `ctx` above the model's
native 262144 also needs a YaRN rope override in `json_model_override_args`, or
SGLang silently clamps back down. See
[`ai-models/qwen3.8-27b-nvfp4-sglang.yaml`](ai-models/qwen3.8-27b-nvfp4-sglang.yaml).

**`sglang-dual` keys**: every SGLang key above, plus the two-node set shared
with `vllm-dual` — `model_path` (this node) / `worker_model_path` (the peer's
path to the same weights; both are bound at `/model` inside the container so
`--model-path` is one string), `worker_host`, `master_addr`, `worker_ip`,
`master_port` (25000), `tp_size` (2), `worker_env`, `head_extra_mounts` /
`worker_extra_mounts`, `ready_timeout` (2400). It launches
`sglang.launch_server --nnodes 2 --node-rank N --dist-init-addr HOST:PORT` on
both ranks; only rank 0 binds the API. Use it when a checkpoint simply does not
fit one node — SGLang cannot offload, so a 125.9 GiB checkpoint on a 121 GiB
box only serves sharded. See
[`ai-models/qwen3.8-flash-next-nvfp4-dual.yaml`](ai-models/qwen3.8-flash-next-nvfp4-dual.yaml).

## Cluster mode: two Sparks (`runtime: vllm-dual`)

Serves one model tensor-parallel (TP=2) across two DGX Sparks over the CX7
link, using vLLM's native multi-node launcher (`--nnodes 2` + `--headless`
worker — no Ray). The head container runs on this node and exposes the API;
the worker container is started on the peer over SSH and computes rank 1 of
every forward pass. `lmswitch on/off` manages both nodes; a failed head tears
the worker back down.

Weights can live **once** on either node: give each node its own path to the
same directory (`model_path` / `worker_model_path`) — typically the owning
node's `~/models/...` and the peer's NFS mount of it. Both are mounted at a
canonical `/model` inside the containers.

**vllm-dual keys** (on top of the vLLM keys above):

| Key | Default | Meaning |
|-----|---------|---------|
| `worker_host` | — | SSH alias of the peer node (passwordless) |
| `master_addr` | — | this node's CX7-link IP |
| `worker_ip` | — | peer's CX7-link IP (its `VLLM_HOST_IP`) |
| `master_port` | `25000` | torch distributed rendezvous port |
| `nccl.ifname` | — | CX7 interface (also pins `GLOO_SOCKET_IFNAME` — Gloo otherwise grabs arbitrary NICs) |
| `nccl.hca` | — | RDMA device for `NCCL_IB_HCA` |
| `nccl.gid_index` | — | `NCCL_IB_GID_INDEX` |
| `model_path` | — | weights dir on this node (may be an NFS mount) |
| `worker_model_path` | `model_path` | weights dir on the peer |
| `worker_env` | — | env overrides applied only to the worker container |
| `gpu_memory_utilization` | `0.80` | per node — each holds its TP shard |
| `ready_timeout` | `1800` | TP=2 loads are slow on first boot |

Set `enforce_eager: false` in dual YAMLs when the profile relies on cudagraph
capture — the single-node default is eager. And stop local models first: a
loaded model on either node shrinks vLLM's memory budget there. See
[`examples/vllm-dual.yaml`](examples/vllm-dual.yaml).

## Cluster mode: GGUF across two Sparks (`runtime: llama-dual`)

Serves one GGUF split across both nodes with llama.cpp's RPC backend: this
node runs `llama-server`, the peer runs `rpc-server` (started over SSH), and
`--tensor-split` decides how many layers land on each. No containers, no
NCCL. RDMA over the CX7 link is auto-negotiated when both llama.cpp builds
have `-DGGML_RPC=ON -DGGML_RPC_RDMA=ON`.

Weights live **once**, on this node: the head reads the GGUF and pushes the
peer's share over the link, so nothing has to be copied or NFS-mounted. The
peer caches what it receives (`rpc_cache`), so restarts skip the re-push.

**llama-dual keys** (on top of the llama keys above):

| Key | Default | Meaning |
|-----|---------|---------|
| `worker_host` | — | SSH alias of the peer node (passwordless) |
| `worker_ip` | — | peer's CX7-link IP — the RPC endpoint |
| `rpc_port` | `50052` | port `rpc-server` listens on |
| `tensor_split` | even | share per device, e.g. `"0.45,0.55"` |
| `rpc_cache` | `true` | peer caches received tensors under `~/.cache/llama.cpp/rpc` |
| `rpc_threads` | — | peer's CPU threads (`rpc-server -t`) |
| `worker_rpc_bin` | `~/utils/llama.cpp/build/bin/rpc-server` | binary on the peer |

`tensor_split` follows llama-server's device order — local CUDA devices
first, then one `RPC<n>` per `--rpc` endpoint. Confirm with `llama-server
--list-devices --rpc <worker_ip>:<rpc_port>` after a llama.cpp upgrade;
reversing it silently loads the wrong share onto the wrong box. Both nodes
must run the **same llama.cpp commit**.

Readiness waits on `/health`, not just the port: llama-server binds the port
before loading and answers 503 meanwhile, which for a cross-node load is
minutes of "ready" that isn't. `lmswitch off` kills the peer's `rpc-server`
even when the head is already gone — a stranded one keeps its whole share
resident.

**Cluster visibility**: add `CLUSTER_HOSTS="<ssh-alias>"` (comma-separated)
to `ai-models/.lmswitch` on each node. `lmswitch list` then merges the peers'
tables (via `ssh <peer> lmswitch list --json`) under a HOST column, and
toggling a peer's model — by name or table number — delegates over SSH.
Unreachable peers are skipped silently; the local table never breaks.

## Where to get models

Models come from [Hugging Face](https://huggingface.co) into your models
directory; each config's `model:` path is relative to it.

```bash
pip install -U "huggingface_hub[cli]"

# GGUF (llama.cpp) — e.g. Unsloth / bartowski quants; grab the .gguf file(s)
hf download unsloth/Qwen3-4B-GGUF Qwen3-4B-Q4_K_M.gguf \
  --local-dir ~/models/unsloth/Qwen3-4B-GGUF
#   → model: "unsloth/Qwen3-4B-GGUF/Qwen3-4B-Q4_K_M.gguf"

# vLLM (safetensors, incl. FP8 / NVFP4 quants) — grab the whole repo into a dir
hf download nvidia/Qwen3.5-MoE-...-NVFP4 --local-dir ~/models/nvidia/qwen3-...-nvfp4
#   → model: "nvidia/qwen3-...-nvfp4"
```

Good sources: `unsloth/`, `bartowski/`, `ggml-org/` for GGUF; the upstream model
repos and `nvidia/` (FP8 / NVFP4) for vLLM. Multi-shard GGUF
(`*-00001-of-0000N.gguf`) is detected automatically — point `model:` at the
first shard.

## How it works

- **GGUF** → `llama-server` is launched as a detached background process; its PID
  and full output go to `ai-models/running/<name>` and `…/<name>.log`. `-fit off`
  is passed by default (see `fit:` above).
- **vLLM** → `docker run -d --name vllm-<name> --gpus all --network host …`; any
  stale/exited container of the same name is `docker rm -f`'d first to avoid a
  name conflict.
- **Readiness** → after launch it polls `http://localhost:<port>/v1/models` until
  it answers (`Ready on port <port>`), the process/container dies
  (`✗ … exited during startup` + a pointer to the log / `docker logs`), or
  `ready_timeout` elapses (`WARNING`).
- **RAM guard** → before launching, free RAM (`MemAvailable` from `/proc/meminfo`)
  is compared to an estimate: `gpu_memory_utilization × total` for vLLM, on-disk
  weight size × 1.3 for GGUF. If short, the start is refused unless `force: true`.
- **Config sync** → each enabled target gets the currently-serving models, all
  pointing at `http://<SPARK_HOST>:<port>/v1`: **opencode** one provider per
  model, **hermes** the active model + a `custom_providers` entry per model (so
  they show in `/model`), **grok** one `[model.<id>]` table per model.
  `SPARK_HOST` is a constant in [`lmswitch.system.io`](lmswitch/system/io.py)
  (`spark-8912.local`) — change it if your host differs. See [Config sync](#config-sync).

## Config sync

lmswitch keeps your coding agents' configs honest: on every `on` / `off` /
toggle / `sync` it rewrites the **currently-serving** models into each enabled
target, every endpoint pointing at `http://<SPARK_HOST>:<port>/v1`. Your agent
always sees the models that are actually up — right ports, right names — with no
hand-editing and no calls to a model that isn't loaded. `SPARK_HOST` is a
constant defined in the package (`lmswitch.system.io.SPARK_HOST`); set it to your
serving host.

Targets are chosen during `lmswitch init` and stored as `SYNC_OPENCODE` /
`SYNC_HERMES` / `SYNC_GROK` in `ai-models/.lmswitch` (only configs that exist on
disk are touched; a target with no config is skipped). Each shapes its own file:

- **[opencode](https://opencode.ai)** → `~/.config/opencode/opencode.json` gets
  one provider per serving model. If `~/.local/share/opencode-export/` exists, a
  copy is written there too, so a remote client (a laptop/Mac over Tailscale,
  LAN, or a Samba mount) can pick up the same config and point straight at the
  serving host.
- **[hermes](https://github.com/NousResearch/hermes-agent)** →
  `~/.hermes/config.yaml`. Hermes runs one active model, so the `model:` block is
  set to the serving model and kept **sticky** (only switched when the current
  one stops); a vision model (id containing `vl`) is wired into
  `auxiliary.vision`. Every serving model is also registered under
  `custom_providers:` (with `discover_models: false`, so the picker doesn't
  live-probe and hang) — that's what makes them all selectable from hermes'
  `/model`. Custom providers you added by hand (pointing at other hosts) are
  preserved. Because discovery is off, `/model` reflects the last sync — re-run
  `lmswitch sync` (or just toggle) after starting a model to refresh the list.
- **[grok](https://github.com/xai-org/grok-cli)** → `~/.grok/config.toml` gets
  one `[model.<id>]` table per serving model; all your other grok settings
  (`[cli]`, `[ui]`, marketplace, the `[models] default`, …) are left untouched.

Run `lmswitch sync` to regenerate on demand — handy after a detached load
finishes.

## Architecture

lmswitch is a small Python package with deliberate module boundaries. Runtimes
are **pluggable**: adding a new backend (sglang, TGI, …) means writing one file
that subclasses `BaseRuntime` and registering it — no changes to the CLI, sync,
or loader code.

```
lmswitch/
├── __main__.py          # `python -m lmswitch`
├── cli.py               # arg parsing, table rendering, interactive TUI, commands
├── sync.py              # opencode / hermes / grok config sync
├── models/
│   └── loader.py        # discover & parse ai-models/*.yaml → model dicts
├── runtimes/            # how a model is started / stopped / probed
│   ├── base.py          #   BaseRuntime ABC + RuntimeRegistry
│   ├── llama.py         #   GGUF via llama-server (detached background process)
│   ├── vllm.py          #   vLLM via Docker
│   ├── sglang.py        #   SGLang via Docker
│   ├── sglang_dual.py   #   SGLang TP=2 across two nodes (head + ssh worker)
│   ├── systemd.py       #   restart: on-failure → systemd user unit
│   └── wait.py          #   readiness polling
└── system/
    ├── io.py            # paths, constants (SPARK_HOST), YAML, family rules
    ├── checks.py        # port / docker / process-state detection
    └── memory.py        # /proc/meminfo + pre-load RAM guard
```

A toggle flows: `cli` resolves the name → `runtimes.start_model` runs the RAM
guard (`system.memory`) and dispatches to the matching `BaseRuntime` →
`runtimes.wait` polls until the endpoint answers → `sync` rewrites the enabled
agent configs. All filesystem state lives under `ai-models/` (configs,
`.lmswitch`, and `running/` PID files); the `LMSWITCH_DATA_DIR` env var overrides
that root (used by the tests).

## Development & tests

```bash
cd ~/utils/lmswitch
uv venv                            # create .venv from pyproject (Python >=3.10)
uv pip install -e .                # the package (pulls in pyyaml)
uv pip install pytest              # test runner
uv run pytest -q                   # run the whole suite (57 tests)
```

Run a single file / test:

```bash
uv run pytest tests/test_sync.py -q
uv run pytest tests/test_sync.py::test_regen_hermes_keeps_running_default_sticky -q
```

| File | Covers |
|------|--------|
| `tests/test_cli.py` | name/index resolution, rendering, command dispatch, `init` |
| `tests/test_llama_cmd.py` | llama-server command construction |
| `tests/test_vllm_and_abort.py` | vLLM start, readiness, RAM guard, Ctrl-C, opencode sync |
| `tests/test_sync.py` | config sync to opencode / hermes / grok (selection, idempotency, round-trip) |
| `tests/test_process_lifecycle.py` | start → detect-running → stop lifecycle |

All tests are pure unit tests — `subprocess` / Docker / `curl` / ports are
stubbed and configs are written to temp dirs (via `LMSWITCH_DATA_DIR`), so they
run anywhere (no GPU, no models, no Docker) and never touch your real configs.

## License

lmswitch

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