Upload README.md

README.md

@@ -1,3 +1,62 @@
 ---
-license: llama2
+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
+
+