llmware/slim-summary-tiny-tool
SLIM-SUMMARY-TINY-TOOL
<!-- Provide a quick summary of what the model is/does. -->
slim-summary-tiny-tool is a 4KM quantized GGUF version of slim-summary-tiny, providing a small, fast inference implementation, to provide high-quality summarizations of complex business documents, on a small, specialized locally-deployable model with summary output structured as a python list of key points.
The size of the self-contained GGUF model binary is ~700 MB, which is small enough to run locally on a CPU with reasonable inference speed, and has been designed to balance solid quality with fast loading and inference on a local machine.
The model takes as input a text passage, an optional parameter with a focusing phrase or query, and an experimental optional (N) parameter, which is used to guide the model to a specific number of items return in a summary list.
Please see the usage notes at: **slim-summary-tiny**
To pull the model via API:
from huggingfacehub import snapshotdownload snapshotdownload("llmware/slim-summary-tiny-tool", localdir="/path/on/your/machine/", localdiruse_symlinks=False)
Load in your favorite GGUF inference engine, or try with llmware as follows:
from llmware.models import ModelCatalog
# to load the model and make a basic inference model = ModelCatalog().loadmodel("slim-summary-tiny-tool") response = model.functioncall(text_sample)
# this one line will download the model and run a series of tests ModelCatalog().tooltestrun("slim-summary-tiny-tool", verbose=True)
Note: please review **config.json** in the repository for prompt wrapping information, details on the model, and full test set.
Model Card Contact
Darren Oberst & llmware team