| --- |
| license: apache-2.0 |
| --- |
| |
| # SLIM-INTENT-TOOL |
|
|
| <!-- Provide a quick summary of what the model is/does. --> |
|
|
|
|
| **slim-intent-tool** is a 4_K_M quantized GGUF version of slim-intent, providing a small, fast inference implementation, optimized for multi-model concurrent deployment. |
|
|
| [**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. |
|
|
| To pull the model via API: |
|
|
| from huggingface_hub import snapshot_download |
| snapshot_download("llmware/slim-intent-tool", local_dir="/path/on/your/machine/", local_dir_use_symlinks=False) |
| |
| |
| Load in your favorite GGUF inference engine, or try with llmware as follows: |
|
|
| from llmware.models import ModelCatalog |
| |
| # to load the model and make a basic inference |
| model = ModelCatalog().load_model("slim-intent-tool") |
| response = model.function_call(text_sample) |
| |
| # this one line will download the model and run a series of tests |
| ModelCatalog().tool_test_run("slim-intent-tool", verbose=True) |
| |
|
|
| Slim models can also orchestrated as part of a multi-model, multi-step LLMfx calls: |
|
|
| from llmware.agents import LLMfx |
| |
| llm_fx = LLMfx() |
| llm_fx.load_tool("intent") |
| response = llm_fx.intent(text) |
| |
|
|
| 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. |
|
|
|
|
| ## Model Card Contact |
|
|
| Darren Oberst & llmware team |
|
|
| [Any questions? Join us on Discord](https://discord.gg/MhZn5Nc39h) |
|
|
