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llmware/slim-summary-phi-3-gguf
slim-summary-phi-3-gguf is a machine learning model from llmware. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
slim-summary-phi-3 is a finetune of phi-3 mini (3.8B parameters) to implement a function-calling summarization model, and then packaged as 4KM quantized GGUF, providing a small, fast inference implementation, to provi…
Downloads · 30 days
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From the Hugging Face model README
slim-summary-phi-3 is a finetune of phi-3 mini (3.8B parameters) to implement a function-calling summarization model, and then packaged as 4_K_M quantized GGUF, 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 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 to return in a summary list.
Please see the usage notes at: slim-summary
To pull the model via API:
from huggingface_hub import snapshot_download
snapshot_download("llmware/slim-summary-phi-3-gguf", local_dir="/path/on/your/machine/", local_dir_use_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().load_model("slim-summary-phi-3-gguf")
response = model.function_call(text_sample)
# this one line will download the model and run a series of tests
ModelCatalog().tool_test_run("slim-summary-phi-3-gguf", verbose=True)
Note: please review config.json in the repository for prompt wrapping information, details on the model, and full test set.
Darren Oberst & llmware team