File size: 1,908 Bytes
5ef7c6a bb0a108 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | ---
license: apache-2.0
---
# 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
|