from transformers import AutoTokenizer
from optimum.intel.openvino import OVModelForCausalLM

model_id = "OpenVINO/gemma-2-9b-it-int8-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.

Running Model Inference with OpenVINO GenAI

Install packages required for using OpenVINO GenAI.

pip install openvino-genai huggingface_hub

Download model from HuggingFace Hub

import huggingface_hub as hf_hub

model_id = "OpenVINO/gemma-2-9b-it-int8-ov"
model_path = "gemma-2-9b-it-int8-ov"

hf_hub.snapshot_download(model_id, local_dir=model_path)

Run model inference:

import openvino_genai as ov_genai

device = "CPU"
pipe = ov_genai.LLMPipeline(model_path, device)
print(pipe.generate("What is OpenVINO?", max_length=200))

More GenAI usage examples can be found in OpenVINO GenAI library docs and samples

Limitations

Check the original model card for original model card for limitations.

Legal information

The original model is distributed under gemma license. More details can be found in original model card.

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. 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.