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license: apache-2.0 |
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# SLIM-SA-NER-3B-TOOL |
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<!-- Provide a quick summary of what the model is/does. --> |
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**slim-sa-ner-3b-tool** is a 4_K_M quantized GGUF version of slim-sa-ner-3b, providing a small, fast inference implementation, optimized for multi-model concurrent deployment. |
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This model combines two of the most popular traditional classifier capabilities (sentiment analysis and named entity recognition) and re-images them as function calls on a small specialized decoder LLM, generating output in the form of a python dictionary with keys corresponding to sentiment and NER identifiers. |
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[**slim-sa-ner-3b**](https://huggingface.co/llmware/slim-sa-ner-3b) is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling. |
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To pull the model via API: |
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from huggingface_hub import snapshot_download |
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snapshot_download("llmware/slim-sa-ner-3b-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 |
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model = ModelCatalog().load_model("slim-sa-ner-3b-tool") |
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response = model.function_call(text_sample) |
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# this one line will download the model and run a series of tests |
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ModelCatalog().tool_test_run("slim-sa-ner-3b-tool", verbose=True) |
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Slim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls: |
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from llmware.agents import LLMfx |
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llm_fx = LLMfx() |
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llm_fx.load_tool("sa-ner") |
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response = llm_fx.sa_ner(text) |
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Note: please review [**config.json**](https://huggingface.co/llmware/slim-sa-ner-3b-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) |
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