slim-qa-gen-tiny-tool is a 4_K_M quantized GGUF version of slim-qa-gen-tiny, providing a small, fast inference implementation, optimized for multi-model concurrent deployment.
This model implements a generative 'question' and 'answer' (e.g., 'qa-gen') function, which takes a context passage as an input, and then generates as an output a python dictionary consisting of two keys:
`{'question': ['What was the amount of revenue in the quarter?'], 'answer': ['$3.2 billion']} `
The model has been designed to accept one of three different parameters to guide the type of question-answer created:
-- 'question, answer' (generates a standard question and answer),
-- 'boolean' (generates a 'yes-no' question and answer), and
-- 'multiple choice' (generates a multiple choice question and answer).
slim-qa-gen-tiny-tool is a fine-tune of a tinyllama (1b) parameter model, designed for fast, local deployment and rapid testing and prototyping. Please also see slim-qa-gen-phi-3-tool, which is finetune of phi-3, and will provide higher-quality results, at the trade-off of slightly slower performance and requiring more memory.
slim-qa-gen-tiny is the Pytorch version of the model, and suitable for fine-tuning for further domain adaptation.
To pull the model via API:
from huggingface_hub import snapshot_download
snapshot_download("llmware/slim-qa-gen-tiny-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-qa-gen-tiny-tool", temperature=0.5, sample=True)
response = model.function_call(text_sample)
# this one line will download the model and run a series of tests
ModelCatalog().tool_test_run("slim-qa-gen-tiny-tool", verbose=True)
Note: please review config.json in the repository for prompt template information, details on the model, and full test set.
