# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("llmware/slim-category-ov")
model = AutoModelForCausalLM.from_pretrained("llmware/slim-category-ov")Quick Links
slim-category-ov
slim-category-ov is a specialized function calling model with a single mission to look for values in a text, based on an "extract" key that is passed as a parameter. No other instructions are required except to pass the context passage, and the target key, and the model will generate a python dictionary consisting of 'category' key and the classification of information category in the text.
This is an OpenVino int4 quantized version of slim-category-ov, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.
Model Description
- Developed by: llmware
- Model type: tinyllama
- Parameters: 1.1 billion
- Model Parent: llmware/slim-category
- Language(s) (NLP): English
- License: Apache 2.0
- Uses: Extraction of values from complex business documents
- RAG Benchmark Accuracy Score: NA
- Quantization: int4
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="llmware/slim-category-ov")