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* feat(website): split the site, move docs to /docs, add a landing page The Hugo docs site has always been localai.io itself, which left nowhere to explain what LocalAI is or show what the team builds. This adds a separate marketing site at the root and moves the documentation under /docs/. Docs: The existing site keeps its content tree and its Relearn theme, and now builds with baseURL <root>/docs/. Its _index.md, which held a hand written landing page, becomes a real documentation home. Every previously published URL keeps working. GitHub Pages has no server side rewrites, so .github/ci/gen-redirects.sh walks the built docs output and leaves a meta refresh plus a canonical link at each old root path. It covers bare .html files too, which is what keeps /gallery.html alive, and it never overwrites a path the marketing site already owns. Website: A second Hugo site under website/ with its own layouts and no external theme, so the marketing side does not have to fight Relearn's home rooted menu and asset pipeline. CI builds both and merges them into one Pages artifact. The design is derived from the project logo rather than invented: the navy of the triangle, the cyan of the llama, the purple of the speed bars. Those offset bars became the motion signature. The background renders a real depth-anything.cpp depth map as contour lines and switches to a locate-anything.cpp style detection overlay over the engines section. Also included: an /engines/ index driven entirely by data/engines.yaml, a /blog/ section with five posts written from the release notes and the engine benchmark suites, install.sh and a Kubernetes manifest since the site advertises both, and a rule in .agents/ that release preparation now includes a blog post and demo clips. Every figure on the site is derived from the repository or the GitHub API, not from memory. Correcting them against their sources found one error in README.md: voxtral-tts.c is text to speech, not speech to text. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-5 [Bash] [Edit] [Write] [Agent] * feat(website): add a star history chart, rewrite the history post in first person The history post read like a changelog written by a committee. It is now in Ettore's voice, first person, with the admissions left in. The numbers paragraph in particular read like a directory listing. It now says what the figures mean rather than which file they came from. Adds an interactive star history chart, built from the GitHub stargazers API rather than embedded from a third party, so the page makes no external request and cannot break when someone else's service is down. The four releases the post is organised around are marked on the curve, and the labels stack into rows because three of them land within two months of each other. The API stops paginating at 40,000 items, so the curve is measured up to December 2025 and the segment from there to today's total is drawn dashed, labelled as an estimate in the caption and in the tooltip. It is a straight line between two known points, and the chart says so rather than implying it is data. Also drops "marketing site" from the README heading and everywhere else it appeared, and calls it the main site instead. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-5 [Bash] [Edit] [Write] --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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+++ disableToc = false title = "Customizing the Model" weight = 5 url = "/getting-started/customize-model" icon = "rocket_launch"
+++
To customize the prompt template or the default settings of the model, a configuration file is utilized. This file must adhere to the LocalAI YAML configuration standards. For comprehensive syntax details, refer to the [advanced documentation]({{%relref "advanced" %}}). The configuration file can be located either remotely (such as in a Github Gist) or within the local filesystem or a remote URL.
LocalAI can be initiated using either its container image or binary, with a command that includes URLs of model config files or utilizes a shorthand format (like huggingface:// or github://), which is then expanded into complete URLs.
The configuration can also be set via an environment variable. For instance:
local-ai github://owner/repo/file.yaml@branch
MODELS="github://owner/repo/file.yaml@branch,github://owner/repo/file.yaml@branch" local-ai
Here's an example to initiate the phi-2 model:
docker run -p 8080:8080 localai/localai:{{< version >}} https://gist.githubusercontent.com/mudler/ad601a0488b497b69ec549150d9edd18/raw/a8a8869ef1bb7e3830bf5c0bae29a0cce991ff8d/phi-2.yaml
You can also check all the embedded models configurations here.
{{% notice tip %}} The model configurations used in the quickstart are accessible here: https://github.com/mudler/LocalAI/tree/master/embedded/models. Contributions are welcome; please feel free to submit a Pull Request.
The phi-2 model configuration from the quickstart is expanded from https://github.com/mudler/LocalAI/blob/master/examples/configurations/phi-2.yaml.
{{% /notice %}}
Example: Customizing the Prompt Template
To modify the prompt template, create a Github gist or a Pastebin file, and copy the content from https://github.com/mudler/LocalAI/blob/master/examples/configurations/phi-2.yaml. Alter the fields as needed:
name: phi-2
context_size: 2048
f16: true
threads: 11
gpu_layers: 90
mmap: true
parameters:
# Reference any HF model or a local file here
model: huggingface://TheBloke/phi-2-GGUF/phi-2.Q8_0.gguf
temperature: 0.2
top_k: 40
top_p: 0.95
template:
chat: &template |
Instruct: {{.Input}}
Output:
# Modify the prompt template here ^^^ as per your requirements
completion: *template
Then, launch LocalAI using your gist's URL:
## Important! Substitute with your gist's URL!
docker run -p 8080:8080 localai/localai:{{< version >}} https://gist.githubusercontent.com/xxxx/phi-2.yaml
Next Steps
- Visit the [advanced section]({{%relref "advanced" %}}) for more insights on prompt templates and configuration files.
- To learn about fine-tuning an LLM model, check out the [fine-tuning section]({{%relref "features/fine-tuning" %}}).