license: apache-2.0
SLIM-SA-NER-3B-TOOL
slim-sa-ner-3b-tool is a 4_K_M quantized GGUF version of slim-sa-ner-3b, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.
This model combines two of the most popular traditional classifier capabilities (sentiment analysis and named entity recognition) and re-images them as function calls on a small specialized decoder LLM, generating output in the form of a python dictionary with keys corresponding to sentiment and NER identifiers.
slim-sa-ner-3b is part of the SLIM ("Structured Language Instruction Model") 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-sa-ner-3b-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-sa-ner-3b-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-sa-ner-3b-tool", verbose=True)
Slim models can also be loaded even more simply as part of a multi-model, multi-step LLMfx calls:
from llmware.agents import LLMfx
llm_fx = LLMfx()
llm_fx.load_tool("sa-ner")
response = llm_fx.sa_ner(text)
Note: please review config.json in the repository for prompt wrapping information, details on the model, and full test set.
Model Card Contact
Darren Oberst & llmware team