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--- |
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license: gemma |
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license_link: https://choosealicense.com/licenses/gemma/ |
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base_model: google/gemma-2-9b-it |
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base_model_relation: quantized |
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--- |
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# gemma-2-9b-it-int4-ov |
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* Model creator: [google](https://huggingface.co/google) |
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* Original model: [gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) |
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## Description |
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This is [gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) 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). |
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## Quantization Parameters |
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Weight compression was performed using `nncf.compress_weights` with the following parameters: |
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* mode: **int4_asym** |
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* ratio: **1** |
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* group_size: **128** |
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For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html). |
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## Compatibility |
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The provided OpenVINO™ IR model is compatible with: |
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* OpenVINO version 2024.5.0 and higher |
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* Optimum Intel 1.21.0 and higher |
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## Running Model Inference with [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) |
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1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend: |
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``` |
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pip install optimum[openvino] |
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``` |
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2. Run model inference: |
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``` |
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from transformers import AutoTokenizer |
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from optimum.intel.openvino import OVModelForCausalLM |
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model_id = "OpenVINO/gemma-2-9b-it-int4-ov" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = OVModelForCausalLM.from_pretrained(model_id) |
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inputs = tokenizer("What is OpenVINO?", return_tensors="pt") |
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outputs = model.generate(**inputs, max_length=200) |
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text = tokenizer.batch_decode(outputs)[0] |
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print(text) |
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``` |
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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). |
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## Running Model Inference with [OpenVINO GenAI](https://github.com/openvinotoolkit/openvino.genai) |
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1. Install packages required for using OpenVINO GenAI. |
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``` |
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pip install openvino-genai huggingface_hub |
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``` |
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2. Download model from HuggingFace Hub |
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``` |
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import huggingface_hub as hf_hub |
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model_id = "OpenVINO/gemma-2-9b-it-int4-ov" |
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model_path = "gemma-2-9b-it-int4-ov" |
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hf_hub.snapshot_download(model_id, local_dir=model_path) |
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``` |
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3. Run model inference: |
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``` |
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import openvino_genai as ov_genai |
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device = "CPU" |
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pipe = ov_genai.LLMPipeline(model_path, device) |
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print(pipe.generate("What is OpenVINO?", max_length=200)) |
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``` |
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More GenAI usage examples can be found in OpenVINO GenAI library [docs](https://github.com/openvinotoolkit/openvino.genai/blob/master/src/README.md) and [samples](https://github.com/openvinotoolkit/openvino.genai?tab=readme-ov-file#openvino-genai-samples) |
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## Limitations |
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Check the original model card for [original model card](https://huggingface.co/google/gemma-2-9b-it) for limitations. |
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## Legal information |
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The original model is distributed under [gemma](https://choosealicense.com/licenses/gemma/) license. More details can be found in [original model card](https://huggingface.co/google/gemma-2-9b-it). |
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## Disclaimer |
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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. |
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