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gemma-7b-int4-ov

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  1. .gitattributes +1 -0
  2. README.md +67 -0
  3. config.json +29 -0
  4. generation_config.json +7 -0
  5. openvino_detokenizer.bin +3 -0
  6. openvino_detokenizer.xml +242 -0
  7. openvino_model.bin +3 -0
  8. openvino_model.xml +0 -0
  9. openvino_tokenizer.bin +3 -0
  10. openvino_tokenizer.xml +755 -0
  11. special_tokens_map.json +34 -0
  12. tokenizer.json +3 -0
  13. tokenizer.model +3 -0
  14. tokenizer_config.json +1756 -0
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ tokenizer.json filter=lfs diff=lfs merge=lfs -text
README.md ADDED
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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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+ ---
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+ # gemma-7b-int4-ov
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+ * Model creator: [Google](https://huggingface.co/google)
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+ * Original model: [gemma-7b](https://huggingface.co/google/gemma-7b)
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+
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+ ## Description
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+ This is [gemma-7b](https://huggingface.co/google/gemma-7b) 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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+
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+ ## Quantization Parameters
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+
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+ Weight compression was performed using `nncf.compress_weights` with the following parameters:
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+
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+ * mode: **int4_asym**
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+ * ratio: **1**
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+ * group_size: **128**
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+
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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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+
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+
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+ ## Compatibility
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+
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+ The provided OpenVINO™ IR model is compatible with:
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+
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+ * OpenVINO version 2024.4.0 and higher
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+ * Optimum Intel 1.20.0 and higher
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+
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+ ## Running Model Inference
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+
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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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+ ```
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+ pip install optimum[openvino]
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+ ```
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+
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+ 2. Run model inference:
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+
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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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+
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+ model_id = "OpenVINO/gemma-7b-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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+
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+ inputs = tokenizer("What is OpenVINO?", return_tensors="pt")
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+
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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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+
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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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+
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+ ## Limitations
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+
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+ Check the original model card for [original model card](https://huggingface.co/google/gemma-7b) for limitations.
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+
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+ ## Legal information
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+
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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-7b).
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+
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+ ## Disclaimer
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+
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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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+ }
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