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license: apache-2.0 |
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# mpt-7b-int8-ov |
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* Model creator: [Mosaic ML, Inc.](https://huggingface.co./mosaicml) |
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* Original model: [mosaicml/mpt-7b-instruct](https://huggingface.co./mosaicml/mpt-7b-instruct) |
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## Description |
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This is [mosaicml/mpt-7b-instruct](https://huggingface.co./mosaicml/mpt-7b-instruct) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8 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: **INT8_ASYM** |
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* ratio: **1.0** |
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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 2024.1.0 and higher |
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* Optimum Intel 1.16.0 and higher |
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## Running Model Inference |
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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/mpt-7b-int8-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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## Limitations |
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Check the original model card for [limitations](https://huggingface.co./mosaicml/mpt-7b-instruct). |
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## Legal information |
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The original model is distributed under [apache-2.0](https://choosealicense.com/licenses/apache-2.0/) license. More details can be found in [mosaicml/mpt-7b-instruct](https://huggingface.co./mosaicml/mpt-7b-instruct). |
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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. |
