license: apache-2.0
Model Card for Model ID
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
- Developed by: llmware
- Model type: Small, specialized LLM
- Language(s) (NLP): English
- License: Apache 2.0
- Finetuned from model: Tiny Llama 1B
Uses
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>: "
=
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:
- Text Passage Context, and
- 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!