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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** 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 has been fine-tuned for **sentiment analysis** function calls, with output of JSON dictionary corresponding to specific named entity keys.  

Each slim model has a corresponding 'tool' in a separate repository, e.g., 'slim-sentiment-tool', which a 4-bit quantized gguf version of the model that is intended to be used for inference.  




### Model Description

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

- **Developed by:** llmware
- **Model type:** Small, specialized LLM
- **Language(s) (NLP):** English
- **License:** Apache 2.0
- **Finetuned from model:** Tiny Llama 1B

## 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>: "

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## How to Get Started with the Model

The fastest way to get started with BLING is through direct import in transformers:

from transformers import AutoTokenizer, AutoModelForCausalLM  
tokenizer = AutoTokenizer.from_pretrained("slim-sentiment")  
model = AutoModelForCausalLM.from_pretrained("slim-sentiment")  


The BLING model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as:

full_prompt = "\<human>\: " + my_prompt + "\n" + "\<bot>\:"

The BLING model was fine-tuned with closed-context samples, which assume generally that the prompt consists of two sub-parts:

1.  Text Passage Context, and
2.  Specific question or instruction based on the text passage

To get the best results, package "my_prompt" as follows:

my_prompt = {{text_passage}} + "\n" + {{question/instruction}}



## Model Card Contact

Darren Oberst & llmware team

Please reach out anytime if you are interested in this project and would like to participate and work with us!