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@@ -7,14 +7,14 @@ 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 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 ("**S**tructured **L**anguage **I**nstruction **M**odel") 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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  Load in your favorite GGUF inference engine, or try with llmware as follows:
@@ -22,11 +22,11 @@ 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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  # to load the model and make a basic inference
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- model = ModelCatalog().load_model("slim-sentiment-tool")
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  response = model.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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  Slim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:
@@ -34,11 +34,11 @@ Slim models can also be loaded even more simply as part of a multi-model, multi-
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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 Card Contact
 
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  <!-- Provide a quick summary of what the model is/does. -->
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+ **slim-intent-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-intent**](https://huggingface.co/llmware/slim-intent) is part of the SLIM ("**S**tructured **L**anguage **I**nstruction **M**odel") 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-intent-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False)
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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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  # to load the model and make a basic inference
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+ model = ModelCatalog().load_model("slim-intent-tool")
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  response = model.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-intent-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("intent")
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+ response = llm_fx.intent(text)
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+ Note: please review [**config.json**](https://huggingface.co/llmware/slim-intent-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 Card Contact