--- license: apache-2.0 license_link: https://choosealicense.com/licenses/apache-2.0/ --- # whisper-tiny-int4-ov * Model creator: [Openai](https://huggingface.co/openai) * Original model: [whisper-tiny](https://huggingface.co/openai/whisper-tiny) ## Description This is [whisper-tiny](https://huggingface.co/openai/whisper-tiny) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT4 by [NNCF](https://github.com/openvinotoolkit/nncf). ## Quantization Parameters Weight compression was performed using `nncf.compress_weights` with the following parameters: * mode: **int4_asym** * ratio: **1** * group_size: **128** For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html). ## Compatibility The provided OpenVINO™ IR model is compatible with: * OpenVINO version 2024.4.0 and higher * Optimum Intel 1.20.0 and higher ## Running Model Inference 1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend: ``` pip install optimum[openvino] ``` 2. Run model inference: ``` from transformers import AutoTokenizer from optimum.intel.openvino import OVModelForCausalLM model_id = "OpenVINO/whisper-tiny-int4-ov" tokenizer = AutoTokenizer.from_pretrained(model_id) model = OVModelForCausalLM.from_pretrained(model_id) inputs = tokenizer("What is OpenVINO?", return_tensors="pt") outputs = model.generate(**inputs, max_length=200) text = tokenizer.batch_decode(outputs)[0] print(text) ``` For more examples and possible optimizations, refer to the [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html). ## Limitations Check the original model card for [original model card](https://huggingface.co/openai/whisper-tiny) for limitations. ## Legal information The original model is distributed under [apache-2.0](https://choosealicense.com/licenses/apache-2.0/) license. More details can be found in [original model card](https://huggingface.co/openai/whisper-tiny). ## Disclaimer Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See [Intel’s Global Human Rights Principles](https://www.intel.com/content/dam/www/central-libraries/us/en/documents/policy-human-rights.pdf). Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.