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@@ -28,18 +28,19 @@ Load in your favorite GGUF inference engine, or try with llmware as follows:
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  # this one line will download the model and run a series of tests
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  ModelCatalog().tool_test_run("slim-sentiment-tool", verbose=True)
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- Note: please review [**config.json**](https://huggingface.co/llmware/slim-sentiment-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.
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-
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  Slim models can also be loaded even more simply as part of a multi-model, multi-step 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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  <!-- Provide a longer summary of what this model is. -->
@@ -50,27 +51,6 @@ Slim models can also be loaded even more simply as part of a multi-model, multi-
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  - **License:** Apache 2.0
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  - **Quantized from model:** llmware/slim-sentiment (finetuned tiny llama 1b)
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- ## Uses
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-
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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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-
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- SLIM models provide a fast, flexible, intuitive way to integrate classifiers and structured function calls into RAG and LLM application workflows.
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- Model instructions, details and test samples have been packaged into the config.json file in the repository, along with the GGUF file.
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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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-
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- All of the SLIM models use a novel prompt instruction structured as follows:
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-
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- "<human> " + text + "<classify> " + keys + "</classify>" + "/n<bot>: "
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-
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  ## Model Card Contact
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  # this one line will download the model and run a series of tests
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  ModelCatalog().tool_test_run("slim-sentiment-tool", verbose=True)
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  Slim models can also be loaded even more simply as part of a multi-model, multi-step 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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+ Note: please review [**config.json**](https://huggingface.co/llmware/slim-sentiment-tool/blob/main/config.json) in the repository for prompt wrapping information, details on the model, and full test set.
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  ### Model Description
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  <!-- Provide a longer summary of what this model is. -->
 
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  - **License:** Apache 2.0
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  - **Quantized from model:** llmware/slim-sentiment (finetuned tiny llama 1b)
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  ## Model Card Contact
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