metadata
license: apache-2.0
Model Card for Model ID
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
- Developed by: llmware
- Model type: GGUF
- Language(s) (NLP): English
- License: Apache 2.0
- Quantized from model: llmware/slim-sentiment (finetuned tiny llama)
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>: "
Model Card Contact
Darren Oberst & llmware team