# Fresher Lemonade: CLI tips and tricks > Source: > Published: 2026-10-16 20:23:24+00:00 I've written before about getting started using [Lemonade](https://lemonade-server.ai/?ref=admcpr.com) for local AI with [VSCode](https://admcpr.com/easy-local-copilot-with-vs-code-and-lemonade/) and [OpenClaw](https://admcpr.com/local-agents-with-lemonade-and-openclaw/). Today I want to run through the CLI commands I use to get the most out of my Lemonade install, from installing custom models to benchmarking, using custom llama.cpp versions and more. ## list Let's start at the beginning with a list of all the Lemonade [approved models](https://lemonade-server.ai/models.html?ref=admcpr.com) that you can install. You can browse what's available on the website but we're on the CLI today so we see them all with `lemonade list`. And of course list supports filtering with wildcards so we can see, for example, only the Gemma 4 MTP models: ``` lemonade list gemma*4*mtp ``` ## pull Loading any of the approved models is easy: we can just `lemonade load Gemma-4-12B-it-MTP-GGUF` and the model is pulled and loaded. What if we want to load a Hugging Face model that hasn't been approved like this [QAT version of Gemma](https://huggingface.co/unsloth/gemma-4-12B-it-qat-GGUF?ref=admcpr.com)? In that case we can simply point the `lemonade pull` command at a Hugging Face path: ``` lemonade pull unsloth/gemma-4-12B-it-qat-GGUF:UD-Q4_K_XL ``` ## load And once it's downloaded we can load the model by name with `lemonade load`. ``` lemonade load gemma-4-12B-it-qat-GGUF-UD-Q4_K_XL ``` ## status Now it's loaded, how about we check the status of our Lemonade server and the models it's loaded using `lemonade status`. ## --ctx-size By default Lemonade will auto configure the context size (Ctx) for us and we can see it's set to `262144` for me in the screenshot above. But we can use the `--ctx-size` switch to set that manually. ``` lemonade load gemma-4-12B-it-qat-GGUF-UD-Q4_K_XL --ctx-size 131072 ``` ## --llamacpp-args Most model providers on Hugging Face provide recommended best practices for running the model you've downloaded. For the Gemma model we're working with you can see them [here](https://huggingface.co/unsloth/gemma-4-12B-it-qat-GGUF?ref=admcpr.com#best-practices) and we can see that they recommend setting `temperature`, `top_p` and `top_k` for best results, we can pass these when we load the model with `--llamacpp-args`. ``` lemonade load gemma-4-12B-it-qat-GGUF-UD-Q4_K_XL --llamacpp-args "--temperature 1.0 --top_p 0.95 --top_k 64" ``` ## --save-options But we don't want to have to type out a complicated set of options every time we load this model so let's use the `--save-options` switch to set the default set of options for this model: ``` lemonade load gemma-4-12B-it-qat-GGUF-UD-Q4_K_XL --llamacpp-args "--temperature 1.0 --top_p 0.95 --top_k 64" --ctx-size 131072 --save-options ``` Now we can just do: ``` lemonade load gemma-4-12B-it-qat-GGUF-UD-Q4_K_XL ``` And our favourite options are automatically populated. ## bench Now wouldn't it be cool if we could actually measure the performance of the model on our hardware, benchmark the time to first token, tokens per second and VRAM usage across different scenarios? You've guessed it, `bench` does exactly that: ``` lemonade bench gemma-4-12B-it-qat-GGUF-UD-Q4_K_XL --ctx-size 131072 ``` ## Modalities, engines and backends, oh my! We're about to start working with these so let's define them quickly. Lemonade supports many modalities (kinds of input and output data), you can see them all in the [Supported Configurations](https://github.com/lemonade-sdk/lemonade?ref=admcpr.com#supported-configurations) section of its readme. We've been working with the *Text generation* modality so far, and the supported engine, backends, devices and operating system configurations for that look like this today: We can see that for *Text generation* Lemonade currently supports 6 *Engines* (llamacpp, llamacpp-hrx, flm, ryzenai-llm, vllm and ds4) and each Engine runs on a Backend, support for which is determined by a combination of our Device and OS. Each time you run a model, behind the scenes Lemonade will evaluate your hardware and OS and automatically select the best Engine and Backend combination. That's great for getting started but what if we want to control which Engine or Backend we're using? `Recipes` for what are now called `Engines`. The rename hasn't been completed everywhere yet so I'm going to refer to `Engines/Recipes` from now on: they're the same thing. ### backends OK so let's take a look at what Engines/Recipes our current platform supports. ``` lemonade backends ``` As I'm running Ubuntu on a Strix Halo you can see I've got a lot of options available to install. Lemonade selected `vulkan` as the backend for `llamacpp` but I can see that `rocm` is also available so let's install that. ``` lemonade backends install llamacpp:rocm ``` ## --llamacpp With our new backend installed let's use it to run our model with the `--llamacpp` option. ``` lemonade load gemma-4-12B-it-qat-GGUF-UD-Q4_K_XL --llamacpp rocm --llamacpp-args "--temperature 1.0 --top_p 0.95 --top_k 64" --ctx-size 131072 ``` And that `bench` command is now going to come in particularly handy because when we run it Lemonade will benchmark all the installed backends so we can compare performance across them. ``` lemonade bench gemma-4-12B-it-qat-GGUF-UD-Q4_K_XL --ctx-size 131072 ``` And we can see that for this model Rocm is faster in TTFT and uses slightly less VRAM but Vulkan wins on TPS. ## config We can get all Lemonade's config settings with, unsurprisingly, `lemonade config`. This will spit out every config setting. Below I'm showing a snip of the `[llamacpp]` settings it outputs for me but you'll see a lot more. ## config set We can also set individual values in the config. I find this particularly useful if I want to test out a newer version of one of the backends, for example to switch to the latest upstream version of ROCm: ``` lemonade config set llamacpp.rocm_bin="latest" ``` This will update the config and, if necessary, download the binary so that next time I run a model with `rocm` it's going to use the latest version. And of course this works for other binaries and I can specify upstream tags, for example: ``` lemonade config set llamacpp.cuda_bin="b8664" ``` And if I want to go back to the built-in default I can just: ``` lemonade config set llamacpp.cuda_bin="builtin" ``` ## --help-all Finally, I wouldn't know any of this if it wasn't for the awesome [lemonade CLI](https://lemonade-server.ai/docs/guide/cli/?ref=admcpr.com) docs and the great help built into the CLI, which is available for every command. ``` lemonade --help-all ``` If I've missed your favourite CLI command or option [let me know.](https://admcpr.com/contact/)