---
license: apache-2.0
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# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

**slim-ner-tool** is part of the SLIM ("Structured Language Instruction Model") model series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.  

slim-ner-tool is a 4_K_M quantized GGUF version of slim-ner, providing a small, fast inference implementation.

Load in your favorite GGUF inference engine (see details in config.json to set up the prompt template), or try with llmware as follows:

    from llmware.models import ModelCatalog

    # to load the model and make a basic inference
    ner_tool = ModelCatalog().load_model("slim-ner-tool")
    response = ner_tool.function_call(text_sample)  

    # this one line will download the model and run a series of tests
    ModelCatalog().test_run("slim-ner-tool", verbose=True)  
    
    
Slim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:

    from llmware.agents import LLMfx

    llm_fx = LLMfx()
    llm_fx.load_tool("ner")
    response = llm_fx.named_entity_extraction(text)


### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** llmware
- **Model type:** GGUF 
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Quantized from model:** llmware/slim-sentiment (finetuned tiny llama)

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

SLIM models provide a fast, flexible, intuitive way to integrate classifiers and structured function calls into RAG and LLM application workflows.  

Model instructions, details and test samples have been packaged into the config.json file in the repository, along with the GGUF file.   



## Model Card Contact

Darren Oberst & llmware team