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@@ -6,21 +6,30 @@ 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, providing a small, fast inference implementation.
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- Load in your favorite GGUF inference engine (see details in config.json to set up the prompt template), or try with llmware as follows:
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- from llmware.models import ModelCatalog
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  # to load the model and make a basic inference
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  sentiment_tool = ModelCatalog().load_model("slim-sentiment-tool")
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  response = sentiment_tool.function_call(text_sample)
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  # this one line will download the model and run a series of tests
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- ModelCatalog().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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@@ -39,7 +48,7 @@ Slim models can also be loaded even more simply as part of a multi-model, multi-
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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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  <!-- Provide a quick summary of what the model is/does. -->
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+ **slim-sentiment-tool** is a 4_K_M quantized GGUF version of slim-sentiment, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.
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+ [**slim-sentiment**](https://huggingface.co/llmware/slim-sentiment) is part of the SLIM ("Structured Language Instruction Model") series, providing a set of small, specialized decoder-based LLMs, fine-tuned for function-calling.
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+ To pull the model via API:
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+ from huggingface_hub import snapshot_download
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+ snapshot_download("llmware/slim-sentiment-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)
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+
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+
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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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+
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  # to load the model and make a basic inference
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  sentiment_tool = ModelCatalog().load_model("slim-sentiment-tool")
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  response = sentiment_tool.function_call(text_sample)
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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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+
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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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  Slim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:
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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 1b)
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  ## Uses
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