Computational Model Studio · Model release
SmolVLM Instruct
Answers visual questions and describes images in text with a compact vision-language model.
Developer: Hugging Face · Last verified:
What it is designed to do
Interpret supplied images in the context of text prompts and generate grounded text descriptions or answers.
- Primary contribution
- perception
- Input
- image, text
- Output
- text (visual question answer, image description)
Documented uses: Visual question answering and image captioning with one or more images and interleaved text.
Access and deployment
- SmolVLM Instruct publisher weights — available. Public Hugging Face publisher repository with safetensors checkpoint and processor files. Artifact documentation
- laptop local: GPU-equipped laptop with compatible Transformers runtime and at least 5.02 GB available GPU memory; 5.02 GB GPU RAM; Publisher benchmark minimum for image-and-text inference; memory can rise with image count, resolution, prompt length and runtime. (publisher stated measurement) Limits: No named laptop configuration was benchmarked in the reviewed post; CPU-only laptop memory and speed were not established; The 5.02 GB value is a workload-bound minimum, not a guarantee for all inputs.
Technical profile and review notes
- Construction
- learned
- Representation
- neural
- Normalized architecture tags
- No normalized architecture tag assigned
- Lifecycle
- Unknown from reviewed sources
Inclusion rationale: Adds image-and-text-to-text interpretation, distinct from image segmentation and image generation.; Publisher reports a bounded GPU-memory minimum and explicitly discusses laptop use.
Limitations
- The publisher states that this model does not generate images.
- The publisher excludes high-stakes and critical decisions affecting well-being or livelihood; generated text may be inaccurate.
- The 5.02 GB GPU-memory figure is a publisher benchmark minimum for specific inference settings, not a general guarantee for every laptop, image count, or prompt length.
Sources and related Studio records
Sources
Connections
- In areaComputational Models
- Supports functionAnswer visual questions
- ArtifactSmolVLM Instruct publisher weights