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llmware/slim-xsum-phi-3-gguf
slim-xsum-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-xsum-phi-3-gguf is a fine-tune of Phi-3 that implements an 'extreme summarization' (e.g., 'xsum') function call based on the parameter key "xsum" that generates an LLM text output in the form of a python dictiona…
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From the Hugging Face model README
slim-xsum-phi-3-gguf is a fine-tune of Phi-3 that implements an 'extreme summarization' (e.g., 'xsum') function call based on the parameter key "xsum" that generates an LLM text output in the form of a python dictionary as follows:
`{'xsum': ['Stock Market declines on worries of interest rates.']} `
The intent of SLIMs is to forge a middle-ground between traditional encoder-based classifiers and open-ended API-based LLMs through the use of function-calling and small specialized LLMs.
slim-xsum is the Pytorch version of the model, and suitable for fine-tuning for further domain adaptation.
To pull the model via API:
from huggingface_hub import snapshot_download
snapshot_download("llmware/slim-xsum-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-xsum-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-xsum-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