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README.md

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 ---
 license: apache-2.0
 ---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+**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
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** llmware
+- **Model type:** Small, specialized LLM
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model:** Tiny Llama 1B
+
+## 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>: "
+
+=
+## 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:
+
+1.  Text Passage Context, and
+2.  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!
+
+
+

config.json

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+{
+  "model_type": "llama"
+}

slim-sentiment.gguf

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+size 668787680