README.md

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license: apache-2.0
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# Model Card for Model ID

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

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

Load in your favorite GGUF inference engine, or try with llmware as follows:

 from llmware.models import ModelCatalog

 sentiment_tool = ModelCatalog().load_model("llmware/slim-sentiment-tool")
 response = sentiment_tool.function_call(text_sample, params=["sentiment"], function="classify")

Slim models can also be loaded even more simply as part of LLMfx calls:

 from llmware.agents import LLMfx

 llm_fx = LLMfx()
 llm_fx.load_tool("sentiment")
 response = llm_fx.sentiment(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. -->

The intended use of SLIM models is to re-imagine traditional 'hard-coded' classifiers through the use of function calls. 

Example:

 text = "The stock market declined yesterday as investors worried increasingly about the slowing economy." 

 model generation - {"sentiment": ["negative"]}

 keys = "sentiment"

All of the SLIM models use a novel prompt instruction structured as follows:

 "<human> " + text + "<classify> " + keys + "</classify>" + "/n<bot>: "


## Model Card Contact

Darren Oberst & llmware team