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@@ -6,24 +6,15 @@ license: apache-2.0
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  <!-- Provide a quick summary of what the model is/does. -->
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- **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.
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- slim-sentiment-tool is a 4_K_M quantized GGUF version of slim-sentiment-tool, providing a fast, small inference implementation.
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  Load in your favorite GGUF inference engine, or try with llmware as follows:
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  from llmware.models import ModelCatalog
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- sentiment_tool = ModelCatalog().load_model("llmware/slim-sentiment-tool")
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- response = sentiment_tool.function_call(text_sample, params=["sentiment"], function="classify")
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-
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- Slim models can also be loaded even more simply as part of LLMfx calls:
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- from llmware.agents import LLMfx
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- llm_fx = LLMfx()
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- llm_fx.load_tool("sentiment")
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- response = llm_fx.sentiment(text)
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  ### Model Description
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  - **Developed by:** llmware
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  - **Model type:** GGUF
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  - **Language(s) (NLP):** English
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- - **License:** Apache 2.0
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- - **Quantized from model:** llmware/slim-sentiment (finetuned tiny llama)
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  ## Uses
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  <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- The intended use of SLIM models is to re-imagine traditional 'hard-coded' classifiers through the use of function calls.
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- Example:
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- text = "The stock market declined yesterday as investors worried increasingly about the slowing economy."
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- model generation - {"sentiment": ["negative"]}
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- keys = "sentiment"
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- All of the SLIM models use a novel prompt instruction structured as follows:
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- "<human> " + text + "<classify> " + keys + "</classify>" + "/n<bot>: "
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  ## Model Card Contact
 
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  <!-- Provide a quick summary of what the model is/does. -->
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+ **dragon-yi-qa-tool** is a Q4_K_M GGUF quantized version of the DRAGON Yi model series, providing a fast, small inference implementation.
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  Load in your favorite GGUF inference engine, or try with llmware as follows:
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  from llmware.models import ModelCatalog
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+ qa_tool = ModelCatalog().load_model("llmware/dragon-yi-qa-tool")
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+ response = qa_tool.inference(question, text_sample)
 
 
 
 
 
 
 
 
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  ### Model Description
 
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  - **Developed by:** llmware
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  - **Model type:** GGUF
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  - **Language(s) (NLP):** English
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+ - **License:** Yi Community License
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+ - **Quantized from model:** llmware/dragon-yi-6b (finetuned yi-6b-base)
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  ## Uses
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  <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+ All of the DRAGON models use the following prompt template:
 
 
 
 
 
 
 
 
 
 
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+ "<human> " + text + "\n" + question + "\n<bot>: "
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  ## Model Card Contact