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| license: cc-by-sa-4.0 |
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| # SLIM-XSUM-TOOL |
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| <!-- Provide a quick summary of what the model is/does. --> |
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| **slim-xsum-tool** is a 4_K_M quantized GGUF version of slim-xsum, providing a small, fast inference implementation, optimized for multi-model concurrent deployment. |
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| This model implements an 'extreme summarization' (e.g., 'xsum') function based on the parameter key "xsum" that generates an LLM text output in the form of a python dictionary as follows: |
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| `{'xsum': ['Stock Market declines on worries of interest rates.']} ` |
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| 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. |
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| [**slim-xsum**](https://huggingface.co/llmware/slim-xsum) is the Pytorch version of the model, and suitable for fine-tuning for further domain adaptation. |
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| To pull the model via API: |
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| from huggingface_hub import snapshot_download |
| snapshot_download("llmware/slim-xsum-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False) |
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| Load in your favorite GGUF inference engine, or try with llmware as follows: |
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| from llmware.models import ModelCatalog |
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| # to load the model and make a basic inference |
| model = ModelCatalog().load_model("slim-xsum-tool") |
| response = model.function_call(text_sample) |
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| # this one line will download the model and run a series of tests |
| ModelCatalog().tool_test_run("slim-xsum-tool", verbose=True) |
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| Note: please review [**config.json**](https://huggingface.co/llmware/slim-xsum-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set. |
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| ## Model Card Contact |
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| Darren Oberst & llmware team |
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| [Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h) |
