| 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 | |