Community-maintained integration. This service is built and maintained by rahulsolanki001. Pipecat does not test or officially support it. Please report issues and request changes on the source repository. Learn more about community integrations.
Overview
SmolVlmService runs HuggingFace SmolVLM
vision-language models locally to describe images flowing through a Pipecat
pipeline. The compact, instruction-tuned models run entirely on your own
hardware — no external API calls, no per-image billing, and no data leaving your
infrastructure. It accepts UserImageRawFrame input and emits a
VisionTextFrame containing the generated description.
Source Repository
Source code, examples, and issues for the SmolVLM integration
PyPI Package
The
pipecat-smolvlm package on PyPISmolVLM Models
Browse the SmolVLM checkpoints on HuggingFace
Installation
This is a community-maintained package distributed separately frompipecat-ai:
~/.cache/huggingface/.
Prerequisites
No accounts or API keys are required — inference runs locally. The service automatically selects the best available device (Intel XPU, CUDA, Apple MPS, then CPU). Passuse_cpu=True to force CPU execution.
Configuration
str
default:"None"
(Deprecated) HuggingFace model identifier. Prefer
settings=SmolVlmService.Settings(model=...).bool
default:"False"
Force CPU execution even when a GPU is available. Useful for reproducibility
or constrained environments.
Settings
Runtime-configurable settings passed via thesettings constructor argument
using SmolVlmService.Settings(...).
Available parameters and defaults are defined by the integration. See the
source repository for
the authoritative, up-to-date list.
Usage
SmolVlmService into a Pipeline wherever a vision service is expected:
text field on the incoming
UserImageRawFrame; the service falls back to settings.default_prompt when it
is empty.
Compatibility
The package targetspipecat-ai>=0.0.100. Check the source
repository for the latest
tested version and changelog.