Ollama also supports multimodal models, which can interact with (but not create) images.
We start by loading the package:
After loading the package, we need to pull a model that can handle
images. For example, the llava
model. Using pull_model("llava") will download the model,
or just load it if it has already been downloaded before.
pull_model("llava")
#> ✔ model llava pulled successfully!We can use textual and visual input together. For instance, we can
ask a question and provide a link to a picture or a local file path,
such as images = "/home/user/Pictures/IMG_4561.jpg".
In the first example, we ask the model to describe the logo of this package:
query(
"Excitedly desscribe this logo",
model = "llava",
images = "https://raw.githubusercontent.com/JBGruber/rollama/master/man/figures/logo.png"
)
#>
#> ── Answer from llava ─────────────────────────────────────────────────
#> This is an image of a logo for "Rollama." The logo features a playful
#> and creative design, with a cartoon-style character resting on a bed
#> of green grass. The character is anthropomorphic, having arms and
#> legs like a human, but it has animal-like ears and is wearing a blue
#> helmet. The helmet seems to have a visor, and there's a badge
#> attached to it that reads "Rollama."
#>
#> The background of the logo is light blue with a faint cloud pattern,
#> which adds to the whimsical feel of the design. The use of bold
#> colors and simple shapes gives the logo a friendly and approachable
#> vibe, suggesting that whatever "Rollama" represents could be fun and
#> enjoyable.
#>
#> Without additional context, it's not possible to determine the exact
#> nature or purpose of Rollama from this image alone. However, the
#> playful design and the badge suggest that it might be related to a
#> game, an application, or possibly a branding for something
#> entertaining and engaging.The second example asks a classification question:
query(
"Which animal is in this image: a llama, dog, or walrus?",
model = "llava",
images = "https://raw.githubusercontent.com/JBGruber/rollama/master/man/figures/logo.png"
)
#>
#> ── Answer from llava ─────────────────────────────────────────────────
#> The image shows an animated character that resembles a llama. It has
#> distinctive features of a llama, such as the large head with two
#> forward-facing horns and a long, curved neck with short, rounded ears
#> at the top.Annotating Several Images with Structured Output
You can combine this setup with the structured output workflow to get a systematic classification of images. First, define the schema for the fields you want back for each image:
animal_schema <- create_schema(
dog = type_boolean(),
cat = type_boolean(),
human = type_boolean(),
bird = type_boolean()
)
animal_schema
#> <rollama structured output schema>
#> ├─object: <NULL> (required)
#> └─properties
#> ├─boolean: <dog> (required)
#> ├─boolean: <cat> (required)
#> ├─boolean: <human> (required)
#> └─boolean: <bird> (required)I put some CC images from Wikipedia in a vector:
examples <- c(
"https://upload.wikimedia.org/wikipedia/commons/thumb/7/7a/Huskiesatrest.jpg/1920px-Huskiesatrest.jpg",
"https://upload.wikimedia.org/wikipedia/commons/thumb/6/68/Orange_tabby_cat_sitting_on_fallen_leaves-Hisashi-01A.jpg/960px-Orange_tabby_cat_sitting_on_fallen_leaves-Hisashi-01A.jpg",
"https://upload.wikimedia.org/wikipedia/commons/c/c4/Puffin_(Fratercula_arctica).jpg"
)To annotate each image on its own, rather than sending all of them
into a single query, pass images as a
list: each element is then paired up with
q (recycling the prompt for every image), producing one
separate query - and one separate result - per image.
results <- query(
q = "What can you see in this image?",
model = "qwen3-vl",
format = animal_schema,
images = as.list(examples),
output = "text",
stream = FALSE,
think = FALSE
)Because the output is guaranteed to be valid JSON, you can parse all results at once1: